Unlocking AI’s Potential: A New Era for Biodiversity Conservation

Scientists from McGill University have made a groundbreaking discovery, revealing the untapped potential of artificial intelligence (AI) to revolutionize biodiversity conservation. A recent study published in Nature Reviews Biodiversity highlights the seven global biodiversity knowledge shortfalls, which hinder our understanding of species distributions and interactions.

“The problem is that we still don’t have basic information about nature, which prevents us from knowing how to protect it,” said Laura Pollock, lead author on the study and assistant professor in McGill’s Department of Biology. “This research aims to bridge this knowledge gap by leveraging AI’s capabilities to analyze vast amounts of biodiversity data.”

The study, a collaboration between computer scientists, ecologists, and an international team of researchers, examines how AI can address the seven global biodiversity knowledge shortfalls. The findings show that AI is currently only being used in two of these areas, leaving significant opportunities untapped.

One example of AI’s potential is BioCLIP, which uses machine learning models to detect species traits from images, aiding in species identification. Additionally, automated insect monitoring platforms like Antenna have helped identify hundreds of new insects.

However, the researchers emphasize that AI can do more. Machine learning models trained on satellite imagery and environmental DNA can map species distributions more accurately than ever before. AI could also help infer species interactions, such as food webs and predator-prey relationships, which remain largely unstudied due to the difficulty of direct observation.

“This research looks at a much broader set of biodiversity questions than previous reviews,” said David Rolnick, co-author of the study, Canada CIFAR AI Chair and assistant professor of computer science at McGill. “It was also surprising to see just how narrowly AI is being applied when it has so much potential to address many of these shortfalls.”

Looking ahead, the research team emphasizes the importance of expanding data-sharing initiatives to improve AI model training, refining algorithms to reduce biases, and ensuring that AI is used ethically in conservation. With global biodiversity targets looming, they say AI, if harnessed effectively, could be one of the most powerful tools available to address the biodiversity crisis.

“AI is changing the way the world works, for better or worse,” said Pollock. “This is one of the ways it could help us.” Protecting biodiversity is crucial because ecosystems sustain human life, and AI can play a vital role in preserving our planet’s precious natural resources.

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Three Phases to a Continent: Unraveling the Mystery of Biodiversity Distribution Across Scales

For decades, ecologists have recognized that the number of species doesn’t increase evenly when moving from local ecosystems to continental scales. This phenomenon has been observed in various regions and species groups, yet the underlying reasons remained unclear until now.

A team of international scientists, including researchers from the German Centre for Integrative Biodiversity Research (iDiv) and the Martin Luther University Halle-Wittenberg (MLU), has developed a new theory to explain the three distinct phases typical of species distributions across scales. This breakthrough, published in the journal Nature Communications, may be crucial for estimating how many species are lost when habitats are destroyed.

As one moves from a small area to the continental scale, the number of species increases. For example, a village pond might host only a handful of amphibian species, but as the scale expands to include rivers and marshes, more frogs, toads, or salamanders appear, reaching several hundred or thousand species at the continental or intercontinental level.

These patterns are known as Species-Area Relationships (SARs). Ecologists have long observed that SARs follow a characteristic three-phase pattern:

1. Phase One (Local to Regional): The number of species increases rapidly in small areas, such as village ponds or local forests.
2. Phase Two (Regional to Continental): As the scale expands, the increase in species slows down, and the rate of new species appearances becomes more gradual.
3. Phase Three (Continental to Intercontinental): Finally, at the continental level, the number of species accelerates once again, with new species appearing at an increasingly rapid pace.

Researchers have now developed a universal theory to explain these three-phase patterns and estimate the number of species at key transition points between the phases. “This is a major step forward in ecology,” says first author Dr Luís Borda-de-Água from the CIBIO research centre in Portugal. “We demonstrated that the individual geographical ranges of all species within the studied areas shape the typical species distribution patterns (SARs) we observe across the globe. By combining these distributions in a novel way, we developed a formula to estimate the number of species at the transitions between different phases.”

Conservation implications of new theory

Such estimates can be crucial for biodiversity conservation. For instance, identifying where the rate of new species appearances changes can help estimate how many species are lost when habitats are destroyed. Such figures form the basis of extinction rate calculations in international biodiversity reports.

To validate their theory, the researchers compared SARs based on observation data from various species groups, such as birds and amphibians, with their calculated estimates, utilising around 700 million observations from a single dataset for their analysis. The strong agreement between data and theory gives scientists great confidence in their approach.

The fascination of ecological theory

“Discovering fundamental principles in ecology is just as thrilling as breakthroughs in physics,” says senior author Prof Henrique Pereira from iDiv and MLU. “New findings like ours unveil hidden patterns that have been shaping life on Earth for millions of years. Just as physics deciphers the universe’s deepest mysteries, new ecological theory can reveal the fundamental forces shaping biodiversity on our amazing planet.”

The article “Newborns living near trees tend to be healthier: New data suggests it’s not because healthier people reside near parks” presents groundbreaking research from Drexel University’s Dornsife School of Public Health. The study aimed to explore the impact of newly planted trees on birth outcomes, controlling for various factors such as education level, income, and body mass index.

Using a unique dataset of over 36,000 trees planted in Portland, Oregon between 1990 and 2020, researchers found a significant association between the number of trees planted within 100 meters of a mother’s home and higher birth weight. The study also discovered that nearby tree planting was linked to lower risks of pre-term birth and small-for-gestational-age birth.

The research revealed that each tree planted within 10 years before a child’s birth was associated with a statistically significant 2.3-gram increase in birthweight. Living within 100 meters of at least 10 trees was associated with about a 50-gram increase in birthweight, which could result in 642 fewer babies being considered small for gestational age.

The authors suggest that established older trees near one’s address may provide more benefits than newly planted trees by also providing psychological restoration and fostering a “soft fascination.” They speculate that this could be due to the developed natural environments reducing stress levels, which are associated with increased likelihood of delivering preterm babies and poor health outcomes later in life.

The study’s findings offer evidence supporting the link between trees and positive birth outcomes. While further research is needed to definitively prove causality, the current study provides valuable insights into the potential benefits of tree planting on public health from an early stage of life.