Tag: AI

The silent invasion: how termites threaten homes worldwide

As climate patterns shift, global cities may soon find themselves under siege by these tiny yet destructive pests.

As climate change continues its relentless march, the world faces not only rising temperatures and extreme weather, but also an insidious threat to our homes: invasive termites. And the bill could be steep – invasive termites currently cost over 40 billion USD annually.

In a new study published in the open-access journal Neobiota, PhD student Edouard Duquesne and Professor Denis Fournier from the Evolutionary Biology & Ecology lab (Université libre de Bruxelles) unveil the unsettling reality of invasive termites’ potential expansion into new territories.

Their research reveals that as temperatures rise and climate patterns shift, cities worldwide, from tropical hotspots like Miami, Sao Paulo, Lagos, Jakarta or Darwin to temperate metropolises like Paris, Brussels, London, New York or Tokyo, could soon find themselves under siege by these tiny yet destructive pests.

A man with a headtorch inspects the damages caused by Coptotermes gestroi termites on a house wall.
Adolfo Cuadrado, a termite infestation specialist at Anticimex, meticulously inspects the damages caused by Coptotermes gestroi on a house wall. © David Mora: https://www.pasiontermitas.com.

But how do termites, typically associated with tropical climates, find their way into cities far beyond their natural habitat? The answer lies in the interconnectedness of our modern world. Urbanisation, with its dense populations and bustling trade networks, provides the perfect breeding ground for termite invasions.

Moreover, the global movement of goods, including wooden furniture transported by private vessels, offers unsuspecting pathways for these silent invaders to hitch a ride into our homes.

“A solitary termite colony, nestled within a small piece of wood, could clandestinely voyage from the West Indies to your Cannes apartment. It might lurk within furniture aboard a yacht moored at the Cannes Film Festival marina.”

“Mating is coming. Termite queens and kings, attracted by lights, may initiate reproduction, laying the groundwork for new colonies to conquer dry land.”

Researchers Edouard Duquesne and Denis Fournier.

Duquesne and Fournier’s research emphasises the need for a paradigm shift in how we approach invasive species modelling. By integrating connectivity variables like trade, transport, and population density, their study highlights the importance of understanding the intricate interactions that facilitate termite spread.

Workers and soldiers of the invasive termite Reticulitermes.
Workers and soldiers of the invasive termite Reticulitermes. © David Mora: https://www.pasiontermitas.com.

In light of these findings, the researchers urge swift action from policymakers and citizens alike. Major cities, regardless of their climate zone, must implement strict termite control measures to protect homes and infrastructure.

“Citizens can play a crucial role by leveraging technology, such as AI-assisted apps like iNaturalist, to detect and report potential termite sightings, turning ordinary residents into vigilant guardians of their environment,” say the researchers.

“As we confront the challenges of a rapidly changing climate, awareness and proactive measures are our best defence against the creeping menace of invasive termites,” they conclude.

Original source

Duquesne E, Fournier D (2024) Connectivity and climate change drive the global distribution of highly invasive termites. NeoBiota 92: 281-314. https://doi.org/10.3897/neobiota.92.115411

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Artificial neural networks could power up curation of natural history collections

Deep learning techniques manage to differentiate between similar plant families with up to 99 percent accuracy, Smithsonian researchers reveal

Millions, if not billions, of specimens reside in the world’s natural history collections, but most of these have not been carefully studied, or even looked at, in decades. While containing critical data for many scientific endeavors, most objects are quietly sitting in their own little cabinets of curiosity.

Thus, mass digitization of natural history collections has become a major goal at museums around the world. Having brought together numerous biologists, curators, volunteers and citizens scientists, such initiatives have already generated large datasets from these collections and provided unprecedented insight.

Now, a study, recently published in the open access Biodiversity Data Journal, suggests that the latest advances in both digitization and machine learning might together be able to assist museum curators in their efforts to care for and learn from this incredible global resource.

A team of researchers from the Smithsonian Department of Botany, Data Science Lab, and Digitization Program Office recently collaborated with NVIDIA to carry out a pilot project using deep learning approaches to dig into digitized herbarium specimens.

Smithsonian researchers classifying digitized herbarium sheets.
Smithsonian researchers classifying digitized herbarium sheets.

Their study is among the first to describe the use of deep learning methods to enhance our understanding of digitized collection samples. It is also the first to demonstrate that a deep convolutional neural network–a computing system modelled after the neuron activity in animal brains that can basically learn on its own–can effectively differentiate between similar plants with an amazing accuracy of nearly 100%.

In the paper, the scientists describe two different neural networks that they trained to perform tasks on the digitized portion (currently 1.2 million specimens) of the United States National Herbarium.

The team first trained a net to automatically recognize herbarium sheets that had been stained with mercury crystals, since mercury was commonly used by some early collectors to protect the plant collections from insect damage. The second net was trained to discriminate between two families of plants that share a strikingly similar superficial appearance.

Sample herbarium specimen image of stained clubmoss
Sample herbarium specimen image of stained clubmoss.

The trained neural nets performed with 90% and 96% accuracy respectively (or 94% and 99% if the most challenging specimens were discarded), confirming that deep learning is a useful and important technology for the future analysis of digitized museum collections.

“The results can be leveraged both to improve curation and unlock new avenues of research,” conclude the scientists.

“This research paper is a wonderful proof of concept. We now know that we can apply machine learning to digitized natural history specimens to solve curatorial and identification problems. The future will be using these tools combined with large shared data sets to test fundamental hypotheses about the evolution and distribution of plants and animals,” says Dr. Laurence J. Dorr, Chair of the Smithsonian Department of Botany.

 

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Original source:

Schuettpelz E, Frandsen P, Dikow R, Brown A, Orli S, Peters M, Metallo A, Funk V, Dorr L (2017) Applications of deep convolutional neural networks to digitized natural history collections. Biodiversity Data Journal 5: e21139. https://doi.org/10.3897/BDJ.5.e21139