Around the world, the total toxicity and ecological harm from agricultural pesticides are rising, despite recent United Nations commitments to halve pesticide use and risks by 2030. The findings establish a global, toxicity-weighted baseline for pesticide use and identify a subset of pesticides, crops, and countries driving the most biodiversity impacts. The widespread use of agricultural pesticides is a growing threat to global biodiversity. To address this concern, the 15^th United Nations Biodiversity Conference set the goal of halving pesticide use and risk by 2030 and recently adopted a new global indicator – total applied toxicity (TAT) – that captures not just how much pesticide is used, but how harmful different chemicals are to living organisms. However, pesticide toxicity varies widely across non-target species; earlier global studies have focused on limited types of pesticides or species or have relied only on usage amounts. They have typically overlooked major differences in toxicity. As a result, the true range of pesticide threats to biodiversity, and the progress made toward UN commitments, remain obscure.

 

Using the TAT approach, Jakob Wolfram and colleagues developed a globally consistent way to measure the ecological harm of pesticides. Instead of relying on standards from a single country, Wolfram et al. used average regulatory safety thresholds per species group and pesticide derived from seven major regulatory authorities worldwide to ensure that the results reflect global conditions. By weighting total pesticide use with these toxicity benchmarks, the authors created a single comprehensive indicator that captures risks of 625 pesticides for a wide range of species. The findings show that the overall ecological toxicity of pesticides is rising worldwide, with increasing trends observed across many countries, crops, and groups of species. Overall, TAT is dominated by only a small number of highly toxic chemicals, with fruits and vegetables, corn, soybean, cereals, and rice accounting for 76-83% of global pesticide toxicity. Moreover, China, Brazil, the United States, and India together contribute 53-68% to the global TAT. According to Wolfram et al., the findings demonstrate that most countries are not on track to meet UN pesticide reduction targets without substantial changes.

In a Policy Forum, Doni Bloomfield and colleagues discuss the need for expanded – yet tailored and flexible – governance for the biological data used to develop powerful artificial intelligence (AI) models. Rapidly advancing AI systems trained on biological data have enabled researchers to design new molecules, predict protein structure and function, and probe vast and highly complex biological datasets for novel insights that could greatly expand our understanding of nature and human health. However, these same tools could also be misused for dangerous purposes, such as designing harmful pathogens or generating genetic sequences that bypass safety checks. Despite these widely recognized risks, current governance is severely lacking, and increasingly powerful models are often released without safety evaluation. Here, Bloomfield et al. discuss how biological data governance could be achieved to both mitigate potential risks of biological AI systems without impeding their research potential. Just as researchers accept limits on access to personal information in genetic datasets in order to protect privacy without halting research, say the authors, similar frameworks could restrict only a narrow class of especially sensitive pathogen data while leaving most scientific data openly available. Such targeted controls would make it harder for malicious actors to obtain the rare and costly datasets needed to train dangerous models, without significantly impeding legitimate research, especially if paired with secure digital research environments. Bloomfield et al. argue that this oversight should, however, remain limited, targeted, and flexible so that governance frameworks can adapt as required to keep up with both technological and scientific advancements. Moreover, to prevent abuse or excessive bureaucratic control, the research community should have the ability to appeal data classifications, and governing agencies should promise to ensure fast, transparent review processes so that much-needed safety measures do not become obstacles to legitimate scientific processes. “Formalizing a system of data access would allow researchers to scrutinize and develop these controls and would give scientists and companies clarity in an environment that is currently somewhat unpredictable,” write the authors. “Starting this work will also allow scientists and governments to learn more about the nature of AI risk and revise data-access controls in light of tangible evidence, rather than guesswork.”