The ethical scientist in a time of uncertainty.

All of science takes place amidst a world shaken by uncertainty, social and political upheaval, and challenges to truthful testimony. Just at the moment in which increasing control over biology has been theorized, our social world has become increasingly contentious and its values more divisive. Using the example of gene drives for malaria control to explore the problem of deep uncertainty in biomedical research, I argue that profound uncertainty is an essential feature. Applying the language and presumptions of the discipline of philosophical ethics, I describe three types of uncertainty that raise ethical challenges in scientific research. Rather than mitigate these challenges with excessive precautions and limits on progress, I suggest that researchers can cultivate classic values of veracity, courage, humility, and fidelity in their research allowing science to proceed ethically under conditions of deep uncertainty.

Revealing uncertainty in the status of biodiversity change.

Biodiversity faces unprecedented threats from rapid global change1. Signals of biodiversity change come from time-series abundance datasets for thousands of species over large geographic and temporal scales. Analyses of these biodiversity datasets have pointed to varied trends in abundance, including increases and decreases. However, these analyses have not fully accounted for spatial, temporal and phylogenetic structures in the data. Here, using a new statistical framework, we show across ten high-profile biodiversity datasets2-11 that increases and decreases under existing approaches vanish once spatial, temporal and phylogenetic structures are accounted for. This is a consequence of existing approaches severely underestimating trend uncertainty and sometimes misestimating the trend direction. Under our revised average abundance trends that appropriately recognize uncertainty, we failed to observe a single increasing or decreasing trend at 95% credible intervals in our ten datasets. This emphasizes how little is known about biodiversity change across vast spatial and taxonomic scales. Despite this uncertainty at vast scales, we reveal improved local-scale prediction accuracy by accounting for spatial, temporal and phylogenetic structures. Improved prediction offers hope of estimating biodiversity change at policy-relevant scales, guiding adaptive conservation responses.

Critical transitions in the Amazon forest system.

The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern1-3. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system1. Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

Companies inadvertently fund online misinformation despite consumer backlash.

The financial motivation to earn advertising revenue has been widely conjectured to be pivotal for the production of online misinformation1-4. Research aimed at mitigating misinformation has so far focused on interventions at the user level5-8, with little emphasis on how the supply of misinformation can itself be countered. Here we show how online misinformation is largely financed by advertising, examine how financing misinformation affects the companies involved, and outline interventions for reducing the financing of misinformation. First, we find that advertising on websites that publish misinformation is pervasive for companies across several industries and is amplified by digital advertising platforms that algorithmically distribute advertising across the web. Using an information-provision experiment9, we find that companies that advertise on websites that publish misinformation can face substantial backlash from their consumers. To examine why misinformation continues to be monetized despite the potential backlash for the advertisers involved, we survey decision-makers at companies. We find that most decision-makers are unaware that their companies' advertising appears on misinformation websites but have a strong preference to avoid doing so. Moreover, those who are unaware and uncertain about their company's role in financing misinformation increase their demand for a platform-based solution to reduce monetizing misinformation when informed about how platforms amplify advertising placement on misinformation websites. We identify low-cost, scalable information-based interventions to reduce the financial incentive to misinform and counter the supply of misinformation online.

Curiosity: primate neural circuits for novelty and information seeking.

For many years, neuroscientists have investigated the behavioural, computational and neurobiological mechanisms that support value-based decisions, revealing how humans and animals make choices to obtain rewards. However, many decisions are influenced by factors other than the value of physical rewards or second-order reinforcers (such as money). For instance, animals (including humans) frequently explore novel objects that have no intrinsic value solely because they are novel and they exhibit the desire to gain information to reduce their uncertainties about the future, even if this information cannot lead to reward or assist them in accomplishing upcoming tasks. In this Review, I discuss how circuits in the primate brain responsible for detecting, predicting and assessing novelty and uncertainty regulate behaviour and give rise to these behavioural components of curiosity. I also briefly discuss how curiosity-related behaviours arise during postnatal development and point out some important reasons for the persistence of curiosity across generations.

Tropical Atlantic multidecadal variability is dominated by external forcing.

The tropical Atlantic climate is characterized by prominent and correlated multidecadal variability in Atlantic sea surface temperatures (SSTs), Sahel rainfall and hurricane activity1-4. Owing to uncertainties in both the models and the observations, the origin of the physical relationships among these systems has remained controversial3-7. Here we show that the cross-equatorial gradient in tropical Atlantic SSTs-largely driven by radiative perturbations associated with anthropogenic emissions and volcanic aerosols since 19503,7-is a key determinant of Atlantic hurricane formation and Sahel rainfall. The relationship is obscured in a large ensemble of CMIP6 Earth system models, because the models overestimate long-term trends for warming in the Northern Hemisphere relative to the Southern Hemisphere from around 1950 as well as associated changes in atmospheric circulation and rainfall. When the overestimated trends are removed, correlations between SSTs and Atlantic hurricane formation and Sahel rainfall emerge as a response to radiative forcing, especially since 1950 when anthropogenic aerosol forcing has been high. Our findings establish that the tropical Atlantic SST gradient is a stronger determinant of tropical impacts than SSTs across the entire North Atlantic, because the gradient is more physically connected to tropical impacts via local atmospheric circulations8. Our findings highlight that Atlantic hurricane activity and Sahel rainfall variations can be predicted from radiative forcing driven by anthropogenic emissions and volcanism, but firmer predictions are limited by the signal-to-noise paradox9-11 and uncertainty in future climate forcings.

Abyssal ocean overturning slowdown and warming driven by Antarctic meltwater.

The abyssal ocean circulation is a key component of the global meridional overturning circulation, cycling heat, carbon, oxygen and nutrients throughout the world ocean1,2. The strongest historical trend observed in the abyssal ocean is warming at high southern latitudes2-4, yet it is unclear what processes have driven this warming, and whether this warming is linked to a slowdown in the ocean's overturning circulation. Furthermore, attributing change to specific drivers is difficult owing to limited measurements, and because coupled climate models exhibit biases in the region5-7. In addition, future change remains uncertain, with the latest coordinated climate model projections not accounting for dynamic ice-sheet melt. Here we use a transient forced high-resolution coupled ocean-sea-ice model to show that under a high-emissions scenario, abyssal warming is set to accelerate over the next 30 years. We find that meltwater input around Antarctica drives a contraction of Antarctic Bottom Water (AABW), opening a pathway that allows warm Circumpolar Deep Water greater access to the continental shelf. The reduction in AABW formation results in warming and ageing of the abyssal ocean, consistent with recent measurements. In contrast, projected wind and thermal forcing has little impact on the properties, age and volume of AABW. These results highlight the critical importance of Antarctic meltwater in setting the abyssal ocean overturning, with implications for global ocean biogeochemistry and climate that could last for centuries.

Human-induced weakening of the Northern Hemisphere tropical circulation.

By accounting for most of the poleward atmospheric heat and moisture transport in the tropics, the Hadley circulation largely affects the latitudinal patterns of precipitation and temperature at low latitudes. To increase our preparednesses for human-induced climate change, it is thus critical to accurately assess the response of the Hadley circulation to anthropogenic emissions1-3. However, at present, there is a large uncertainty in recent Northern Hemisphere Hadley circulation strength changes4. Not only do climate models simulate a weakening of the circulation5, whereas atmospheric reanalyses mostly show an intensification of the circulation4-8, but atmospheric reanalyses were found to have artificial biases in the strength of the circulation5, resulting in unknown impacts of human emissions on recent Hadley circulation changes. Here we constrain the recent changes in the Hadley circulation using sea-level pressure measurements and show that, in agreement with the latest suite of climate models, the circulation has considerably weakened over recent decades. We further show that the weakening of the circulation is attributable to anthropogenic emissions, which increases our confidence in human-induced tropical climate change projections. Given the large climate impacts of the circulation at low latitudes, the recent human-induced weakening of the flow suggests wider consequences for the regional tropical-subtropical climate.

Net greenhouse gas balance of fibre wood plantation on peat in Indonesia.

Tropical peatlands cycle and store large amounts of carbon in their soil and biomass1-5. Climate and land-use change alters greenhouse gas (GHG) fluxes of tropical peatlands, but the magnitude of these changes remains highly uncertain6-19. Here we measure net ecosystem exchanges of carbon dioxide, methane and soil nitrous oxide fluxes between October 2016 and May 2022 from Acacia crassicarpa plantation, degraded forest and intact forest within the same peat landscape, representing land-cover-change trajectories in Sumatra, Indonesia. This allows us to present a full plantation rotation GHG flux balance in a fibre wood plantation on peatland. We find that the Acacia plantation has lower GHG emissions than the degraded site with a similar average groundwater level (GWL), despite more intensive land use. The GHG emissions from the Acacia plantation over a full plantation rotation (35.2 ± 4.7 tCO2-eq ha-1 year-1, average ± standard deviation) were around two times higher than those from the intact forest (20.3 ± 3.7 tCO2-eq ha-1 year-1), but only half of the current Intergovernmental Panel on Climate Change (IPCC) Tier 1 emission factor (EF)20 for this land use. Our results can help to reduce the uncertainty in GHG emissions estimates, provide an estimate of the impact of land-use change on tropical peat and develop science-based peatland management practices as nature-based climate solutions.

The WHO estimates of excess mortality associated with the COVID-19 pandemic.

The World Health Organization has a mandate to compile and disseminate statistics on mortality, and we have been tracking the progression of the COVID-19 pandemic since the beginning of 20201. Reported statistics on COVID-19 mortality are problematic for many countries owing to variations in testing access, differential diagnostic capacity and inconsistent certification of COVID-19 as cause of death. Beyond what is directly attributable to it, the pandemic has caused extensive collateral damage that has led to losses of lives and livelihoods. Here we report a comprehensive and consistent measurement of the impact of the COVID-19 pandemic by estimating excess deaths, by month, for 2020 and 2021. We predict the pandemic period all-cause deaths in locations lacking complete reported data using an overdispersed Poisson count framework that applies Bayesian inference techniques to quantify uncertainty. We estimate 14.83 million excess deaths globally, 2.74 times more deaths than the 5.42 million reported as due to COVID-19 for the period. There are wide variations in the excess death estimates across the six World Health Organization regions. We describe the data and methods used to generate these estimates and highlight the need for better reporting where gaps persist. We discuss various summary measures, and the hazards of ranking countries' epidemic responses.

A well-oxygenated eastern tropical Pacific during the warm Miocene.

The oxygen content of the oceans is susceptible to climate change and has declined in recent decades1, with the largest effect in oxygen-deficient zones (ODZs)2, that is, mid-depth ocean regions with oxygen concentrations <5 µmol kg-1 (ref. 3). Earth-system-model simulations of climate warming predict that ODZs will expand until at least 2100. The response on timescales of hundreds to thousands of years, however, remains uncertain3-5. Here we investigate changes in the response of ocean oxygenation during the warmer-than-present Miocene Climatic Optimum (MCO; 17.0-14.8 million years ago (Ma)). Our planktic foraminifera I/Ca and δ15N data, palaeoceanographic proxies sensitive to ODZ extent and intensity, indicate that dissolved-oxygen concentrations in the eastern tropical Pacific (ETP) exceeded 100 µmol kg-1 during the MCO. Paired Mg/Ca-derived temperature data suggest that an ODZ developed in response to an increased west-to-east temperature gradient and shoaling of the ETP thermocline. Our records align with model simulations of data from recent decades to centuries6,7, suggesting that weaker equatorial Pacific trade winds during warm periods may lead to decreased upwelling in the ETP, causing equatorial productivity and subsurface oxygen demand to be less concentrated in the east. These findings shed light on how warm-climate states such as during the MCO may affect ocean oxygenation. If the MCO is considered as a possible analogue for future warming, our findings seem to support models suggesting that the recent deoxygenation trend and expansion of the ETP ODZ may eventually reverse3,4.

Tropical forests are approaching critical temperature thresholds.

The critical temperature beyond which photosynthetic machinery in tropical trees begins to fail averages approximately 46.7 °C (Tcrit)1. However, it remains unclear whether leaf temperatures experienced by tropical vegetation approach this threshold or soon will under climate change. Here we found that pantropical canopy temperatures independently triangulated from individual leaf thermocouples, pyrgeometers and remote sensing (ECOSTRESS) have midday peak temperatures of approximately 34 °C during dry periods, with a long high-temperature tail that can exceed 40 °C. Leaf thermocouple data from multiple sites across the tropics suggest that even within pixels of moderate temperatures, upper canopy leaves exceed Tcrit 0.01% of the time. Furthermore, upper canopy leaf warming experiments (+2, 3 and 4 °C in Brazil, Puerto Rico and Australia, respectively) increased leaf temperatures non-linearly, with peak leaf temperatures exceeding Tcrit 1.3% of the time (11% for more than 43.5 °C, and 0.3% for more than 49.9 °C). Using an empirical model incorporating these dynamics (validated with warming experiment data), we found that tropical forests can withstand up to a 3.9 ± 0.5 °C increase in air temperatures before a potential tipping point in metabolic function, but remaining uncertainty in the plasticity and range of Tcrit in tropical trees and the effect of leaf death on tree death could drastically change this prediction. The 4.0 °C estimate is within the 'worst-case scenario' (representative concentration pathway (RCP) 8.5) of climate change predictions2 for tropical forests and therefore it is still within our power to decide (for example, by not taking the RCP 6.0 or 8.5 route) the fate of these critical realms of carbon, water and biodiversity3,4.

Fennoscandian tree-ring anatomy shows a warmer modern than medieval climate.

Earth system models and various climate proxy sources indicate global warming is unprecedented during at least the Common Era1. However, tree-ring proxies often estimate temperatures during the Medieval Climate Anomaly (950-1250 CE) that are similar to, or exceed, those recorded for the past century2,3, in contrast to simulation experiments at regional scales4. This not only calls into question the reliability of models and proxies but also contributes to uncertainty in future climate projections5. Here we show that the current climate of the Fennoscandian Peninsula is substantially warmer than that of the medieval period. This highlights the dominant role of anthropogenic forcing in climate warming even at the regional scale, thereby reconciling inconsistencies between reconstructions and model simulations. We used an annually resolved 1,170-year-long tree-ring record that relies exclusively on tracheid anatomical measurements from Pinus sylvestris trees, providing high-fidelity measurements of instrumental temperature variability during the warm season. We therefore call for the construction of more such millennia-long records to further improve our understanding and reduce uncertainties around historical and future climate change at inter-regional and eventually global scales.

Emergent constraints on future precipitation changes.

Future projections of global mean precipitation change (ΔP) based on Earth-system models have larger uncertainties than projections of global mean temperature changes (ΔT)1. Although many observational constraints on ΔT have been proposed, constraints on ΔP have not been well studied2-5 and are often complicated by the large influence of aerosols on precipitation4. Here we show that the upper bound (95th percentile) of ΔP (2051-2100 minus 1851-1900, percentage of the 1980-2014 mean) is lowered from 6.2 per cent to 5.2-5.7 per cent (minimum-maximum range of sensitivity analyses) under a medium greenhouse gas concentration scenario. Our results come from the Coupled Model Intercomparison Project phase 5 and phase 6 ensembles6-8, in which ΔP for 2051-2100 is well correlated with the global mean temperature trends during recent decades after 1980 when global anthropogenic aerosol emissions were nearly constant. ΔP is also significantly correlated with the recent past trends in precipitation when we exclude the tropical land areas with few rain-gauge observations. On the basis of these significant correlations and observed trends, the variance of ΔP is reduced by 8-30 per cent. The observationally constrained ranges of ΔP should provide further reliable information for impact assessments.

The missing risks of climate change.

The risks of climate change are enormous, threatening the lives and livelihoods of millions to billions of people. The economic consequences of many of the complex risks associated with climate change cannot, however, currently be quantified. Here we argue that these unquantified, poorly understood and often deeply uncertain risks can and should be included in economic evaluations and decision-making processes. We present an overview of these unquantified risks and an ontology of them founded on the reasons behind their lack of robust evaluation. These consist of risks missing owing to delays in sharing knowledge and expertise across disciplines, spatial and temporal variations of climate impacts, feedbacks and interactions between risks, deep uncertainty in our knowledge, and currently unidentified risks. We highlight collaboration needs within and between the natural and social science communities to address these gaps. We also provide an approach for integrating assessments or speculations of these risks in a way that accounts for interdependencies, avoids double counting and makes assumptions clear. Multiple paths exist for engaging with these missing risks, with both model-based quantification and non-model-based qualitative assessments playing crucial roles. A wide range of climate impacts are understudied or challenging to quantify, and are missing from current evaluations of the climate risks to lives and livelihoods. Strong interdisciplinary collaboration and deeper engagement with uncertainty is needed to properly inform policymakers and the public about climate risks.

Comprehensive evidence implies a higher social cost of CO2.

The social cost of carbon dioxide (SC-CO2) measures the monetized value of the damages to society caused by an incremental metric tonne of CO2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit-cost analysis for over a decade, SC-CO2 estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine1 (NASEM) highlighted that current SC-CO2 estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO2 estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO2. Our preferred mean SC-CO2 estimate is $185 per tonne of CO2 ($44-$413 per tCO2: 5%-95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government's current value of $51 per tCO2. Our estimates incorporate updated scientific understanding throughout all components of SC-CO2 estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO2 values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

Direct evidence for phosphorus limitation on Amazon forest productivity.

The productivity of rainforests growing on highly weathered tropical soils is expected to be limited by phosphorus availability1. Yet, controlled fertilization experiments have been unable to demonstrate a dominant role for phosphorus in controlling tropical forest net primary productivity. Recent syntheses have demonstrated that responses to nitrogen addition are as large as to phosphorus2, and adaptations to low phosphorus availability appear to enable net primary productivity to be maintained across major soil phosphorus gradients3. Thus, the extent to which phosphorus availability limits tropical forest productivity is highly uncertain. The majority of the Amazonia, however, is characterized by soils that are more depleted in phosphorus than those in which most tropical fertilization experiments have taken place2. Thus, we established a phosphorus, nitrogen and base cation addition experiment in an old growth Amazon rainforest, with a low soil phosphorus content that is representative of approximately 60% of the Amazon basin. Here we show that net primary productivity increased exclusively with phosphorus addition. After 2 years, strong responses were observed in fine root (+29%) and canopy productivity (+19%), but not stem growth. The direct evidence of phosphorus limitation of net primary productivity suggests that phosphorus availability may restrict Amazon forest responses to CO2 fertilization4, with major implications for future carbon sequestration and forest resilience to climate change.

TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses.

Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.

The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies?

Molecular Biology has long tried to discover mechanisms, considering that unless we understand the principles, we cannot develop applications. Now machine learning and artificial intelligence enable direct leaps to application without understanding the principles. Will this herald a decline in mechanistic studies?