UK gardens took a battering this summer as a heat dome settled over western Europe, and much of the green and pleasant land turned a dusty beige.

Some areas went without rain for almost two months. The “joy of gardening” turned into waiting until the cooler evening period, then laboriously trailing out from the kitchen with buckets of water to keep the patio plants alive.

But as the rains begin to return, what can gardeners do to restore their garden’s lustre? And what have we learned as we look around our parched borders and fractured lawns?

Let’s start with the good news. Nature is resilient and many plants may still be alive despite appearing bedraggled, wilted or defoliated. They just implemented strategies that help them survive a drought.

Leaf wilting and abscission is the plant’s natural response to conserving water and protecting its vital phloem (which transports sugars from the leaves to the rest of the plant) and root tissues from desiccation.

So, gardeners don’t need to take the hacksaw to the shrub border yet with the notion of making a fresh start. They can check if individual woody plants are still alive by scraping off the epidermal cells (young bark) of the shoots with a thumbnail. If the phloem tissues below are green, life prevails.

Some of the garden’s woody plants may re-leaf again this autumn – or if they are herbaceous, throw up new shoots from the soil. But many will wait until next spring before investing in a new set of leaves or stems.

Gardeners should avoid the temptation to feed plants up (add fertiliser) now, as such chemicals placed on hard soils will simply wash away with the first rains, rather than be incorporated effectively into the soil. More than anything else, gardeners need patience at this time.

Some plants will have dead stems; these can be pruned out immediately. With perennial plants, though, it is worth hesitating before removing the whole specimen, as shoots can regenerate from the stem base or remaining root system.

Similarly, brown lawns will turn green again. However, if there are residual bare patches, new growth can be encouraged by sowing grass seed mixed in with sand in mid-to-late September.

Plants employ different strategies for survival based on the climate they evolved in, and we see a legacy of this in the cultivated species we use in the garden.

Those from high rainfall areas – such as certain rhododendron, pieris, hydrangea, magnolia and sorbus – struggled this summer. Hydrangea has limited capacity to control moisture loss, whereas more drought-adapted species such as cotinus can precisely regulate their stomatal pores (where CO₂ is absorbed and moisture vapour lost).

Other plants such as forsythia and fuchsia are chance takers, still trying to grow throughout a dry period by keeping their stomata open and just running the risk of water shortage. But these types of plants can come a cropper as water columns within their xylem vessels (which transport water and minerals from the roots to the rest of the plant) snap, causing the whole stem to fail.

Anecdotally, despite the harsh dry, hot conditions, some garden plants performed well this year. Salvia, verbena, lavander and, in my garden’s clay-based soils, roses flowered prolifically, even if their shoot growth was curtailed.

Rejuvenating your garden

For those gardeners keen to rejuvenate their dreary, desolate brown patches quickly, autumn bedding plants can help provide some late season cheer. Plants such as cyclamen, chrysanthemum, aster and winter pansies can be planted now as soil moisture is restored.

After a summer like this, some precious plant specimens will be lost, but many gardens should return to their former glories – if the UK experiences a cooler summer next year.

Over the longer term, as climate change moves the UK’s southern regions to something akin to a Mediterranean climate, some gardeners will need to adapt. Mainly, this will mean planting more Mediterranean flora.

Much more challenging, however, is the climate turbulence that accrues from climate change. This compounding of stresses – such as flooding in early summer followed by drought a few weeks later, not to mention more extreme events such as fire and high wind speed – is a much more difficult phenomenon for plants.

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Read more: A rollercoaster of weather extremes: drought and heatwaves are creating UK’s climate whiplash

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While research on this problem is still in its infancy, early results suggest that gardeners may need to look to plant species with multiple stress tolerances. Some garden plants that are popular in Britain, like buddleia, already have good resilience.

We can also look to new genotypes from the natural world. Plants from dry river beds, for example, cope well with both drought and flooding.

Plantlife with wide natural geographical distributions and weed-like strategies (where they grow and reproduce quickly) should also be brought into breeding programmes now, to help improve climate resilience in the garden plant palette.

Ross Cameron is an advisor to the Royal Horticultural Society (RHS). Some of the research outlined in this article has been funded by the RHS and other environmental charities. Ross does not receive any personal income from these affiliations.

Scientists have crossed an important line in biological engineering. In a recent study, researchers used artificial intelligence to design complete sets of genetic instructions for bacteriophages, viruses that infect bacteria. Some of those computer-generated designs produced working viruses when they were built and tested in the laboratory.

These viruses were designed to infect bacteria rather than people. The researchers worked with bacteriophages that target E. coli, and one of the AI models they used, Evo 2, was developed with safety restrictions. Its developers excluded viruses that infect organisms such as animals, plants and humans from the data used to train it.

Even so, the experiment raises an important question: if AI becomes increasingly able to help design biological systems, are our safeguards developing fast enough?

As engineers working in vaccine manufacturing and public health security, we see good reasons to continue this research. Bacteriophages could potentially be used to treat bacterial infections, including those that are becoming harder to treat with antibiotics. But advances in biological design also create new challenges for biosecurity.

Genome language models work in a broadly similar way to AI language models that learn patterns in text. Instead of learning from words and sentences, they learn patterns in genetic code. Evo 2, for example, was trained on trillions of DNA building blocks taken from many different forms of life.

This gives researchers a powerful new way to study biology and generate possible DNA sequences. It also means that part of biological design can now happen on a computer, before anything has been made in a laboratory.

An AI-generated DNA sequence is still several steps away from becoming a real biological threat. A digital design has to be turned into physical genetic material, assembled correctly and tested under the right laboratory conditions. Each of those stages also creates an opportunity to reduce risk.

One safeguard can be built into the AI model itself. The developers of Evo 2 deliberately removed viruses that infect humans and other similar organisms from its training data for safety reasons. Their research found that the model performed poorly when tested on proteins from viruses that infect humans.

Such precautions will need to be tested and strengthened as AI tools for biology become more capable. Researchers have already argued that AI and biosecurity need to be considered together, so that safeguards develop alongside new capabilities.

Another checkpoint comes when digital DNA is turned into physical DNA. Companies that manufacture DNA to order can check both the requested genetic sequence and the person or organisation ordering it for potential security concerns. Researchers working on DNA synthesis screening have argued for common international approaches as this technology becomes more widely available.

Screening may also need to become more sophisticated. A system designed mainly to recognise DNA that resembles known dangerous pathogens may struggle if AI produces something genuinely new. Recent research on identifying “sequences of concern” has therefore considered what a genetic sequence might do, as well as whether it resembles one already linked to a dangerous organism.

Laboratories and research institutions provide another layer of protection. Potentially risky research can be assessed before experiments begin, including questions about how biological material will be contained, who will have access to it and whether the expected benefits justify the risks.

Research funders can also influence how security risks are managed. UK Research and Innovation (UKRI), for example, has established a Trusted Research and Innovation team to help researchers and institutions identify and manage security risks in collaborative research.

Public health preparedness belongs in this discussion too. If biological design becomes faster and more accessible, health authorities will need to be able to detect and investigate unusual outbreaks quickly. The Metagenomics Surveillance Collaboration and Analysis Programme (mSCAPE), led by the UK Health Security Agency, analyses genetic material from samples to help detect and track emerging pathogens.

The programme is not specifically designed to find AI-created viruses. But its approach is relevant because scientists do not have to know exactly which pathogen they are looking for in advance. Systems capable of detecting unusual or previously unseen organisms could become increasingly valuable as biological technologies develop. Preparedness also means being able to develop diagnostic tests, treatments and vaccines quickly when a new threat appears.

In our view, one of the biggest weaknesses in global biosecurity is that countries are not equally prepared. Some already have established systems for identifying and responding to biological risks, while others are still developing them. We see a similar gap in vaccine regulation, where countries vary considerably in their ability to assess new products. Biological threats can cross borders, so effective preparedness will depend on strengthening these capabilities internationally.

There are difficult trade-offs. Heavy restrictions can slow useful research, including work aimed at understanding disease or developing treatments. Weak safeguards can allow scientific capability to develop faster than the systems intended to prevent misuse.

Open science makes this harder. Evo 2 was released openly, including the code and technical information needed for other researchers to use and develop it. Openness can accelerate discovery and widen access to powerful research tools. But once such tools are widely distributed, controlling how they are used becomes harder.

No single safeguard can deal with all of these risks. Biosecurity will need to operate at several stages, from how biological AI models are developed and released to DNA screening, laboratory oversight and public health preparedness.

The recent bacteriophage study does not show that AI can design a pandemic virus. What it shows is significant enough: AI can already help design complete viral genomes that work when they are physically created.

We have an opportunity to decide what responsible safeguards should look like while this technology is still developing. Waiting for more dangerous capabilities to emerge would leave scientists, governments and public health systems trying to develop protections after they are already needed.

AI is beginning to change what humans can design in biology. Our ability to govern that power needs to develop alongside it.

Tuck Seng Wong receives funding from Department of Health and Social Care (DHSC), UK Research and Innovation (UKRI), and Foreign, Commonwealth & Development Office (FCDO).

Kang Lan Tee receives funding from Department of Health and Social Care (DHSC), UK Research and Innovation (UKRI), and Foreign, Commonwealth & Development Office (FCDO).

Do you ever see comments on social media that seem way off topic, but still manage to wrench the discussion around to divisive political debate?

A discussion about the cost of living suddenly becomes an argument about immigration. A conversation about the war in Ukraine turns into claims about government corruption. It can feel jarring – and sometimes this is deliberate.

As generative AI becomes more powerful, malicious groups are increasingly using it to produce and spread disinformation online. Automated accounts can flood social media with convincing comments designed to sow division, inflame political debate and undermine trust in reliable information.

But our latest research offers a way to spot these attempts. Rather than trying to identify whether a post was written by AI, we focus on something different: whether it’s trying to derail the conversation.

Until recently, identifying malicious accounts was often quite straightforward. Many campaigns relied on people writing in a second language. So, posts sometimes contained grammatical mistakes or unusual word choices. Detection systems could look for these patterns in the language used.

But generative AI has changed that. AI systems can now produce fluent, natural-sounding text that is much harder to distinguish from human writing. For example, patterns like use of em-dashes and the word “delve” used to be telltale signs of a text being generated by AI. But AIs are adapting, and these older systems are increasingly ineffective.

Trying to detect AI purely from the words people use is becoming a losing battle. We believe the better approach is to look at what a message is trying to achieve.

Looking for signs

Attempts to spread disinformation often work by steering conversations away from their original topic, towards more polarising issues. So, instead of analysing individual words, we set out to build a system that could recognise this phenomenon in online discussions.

We analysed comments posted beneath BBC News videos on YouTube, a platform that has previously been targeted by organised disinformation campaigns.

For example, imagine a comment about Ukraine’s president, Volodymyr Zelensky, interacting with senior UK political figures: “Zelensky must be wondering how many foreign secretaries the UK goes through.” Now, imagine another person responding: “Mind you, Zelensky has barely been president for four years. Maybe that’s why the little tyrant bans his opposition.”

Whether that second point is true or false is not the issue. Instead of responding to the original comment, it redirects the conversation towards a different, more divisive topic.

This is known as a red herring: introducing an unrelated issue that distracts from the original discussion. These kinds of shift are difficult for conventional disinformation detection systems to identify, because they are not tied to particular words or phrases.

We manually analysed more than 1,600 comments under BBC News videos, labelling them according to 25 different features of online discussion – and discovered some clear patterns.

We found that 36% of derailing messages had red herrings, 65% had leaps in logic known as “non sequiturs”, and 20% contained personal attacks. They were also much less likely to acknowledge previous comments or express empathy.

Spotting manipulation

The next step was to see whether an AI system could recognise these patterns automatically. We used an AI to catch an AI.

For every genuine online comment, we asked an AI large language model to generate several reasonable, relevant responses. Returning to the example of UK foreign secretaries, the AI suggested replies such as: “The current situation in this country must come as quite a shock” or “One too many?”. Both responded directly to the original point.

The system then compares the real response with our AI-generated replies. If the actual comment differs substantially, it may indicate that someone is attempting to steer the conversation in a different direction. So, rather than searching for suspicious words, our system looks for unexpected changes in the flow of the discussion.

How the system works:

We tested this approach using our manually labelled dataset. In our second study, the system correctly identified derailing comments around 77% of the time.

That’s far from perfect, but no detection system is – particularly when analysing something as complex as human conversation. However, our approach performed around twice as well as existing systems based on word-level sentiment analysis. It also achieved results comparable with the level of agreement between human researchers.

Our approach is effective because the AI learns what a typical response to a conversation looks like. When a reply unexpectedly changes the discussion, the system can identify that change and analyse patterns that earlier methods couldn’t detect.

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Read more: Why science GCSEs matter more than we think in a post-truth age

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Of course, going off topic isn’t necessarily a sign of malicious intent or disinformation. People naturally take conversations in unexpected directions, and there are many legitimate reasons why discussions evolve.

For that reason, this technology may act as an early-warning system rather than a replacement for human judgment. It could help moderators identify conversations that deserve closer attention – but any final decisions should remain with trained experts.

There are also important ethical questions to address. AI systems can reflect biases in the data they are trained on, and they still do not understand conversations in quite the same way that people do. Improving how AI represents and interprets human discussion remains a challenge.

As AI-generated content becomes increasingly difficult to distinguish from human writing, detecting disinformation requires more than simply searching for telltale words. It requires understanding how conversations work, how they are manipulated, and when someone is trying to quietly steer them off course.

Seán Roberts was funded by the AI and autonomy for intelligence, surveillance and reconnaissance (A2ISR) project at the Defence Science and Technology Laboratory (Dstl) through the Defence and Security Accelerator (DASA), which is part of the UK Ministry of Defence, United Kingdom.

Kateryna Krykoniuk was funded by the AI and autonomy for intelligence, surveillance and reconnaissance (A2ISR) project at the Defence Science and Technology Laboratory (Dstl) through the Defence and Security Accelerator (DASA), which is part of the UK Ministry of Defence, United Kingdom.