Helping Nature with Artificial Intelligence

A look at how AI can aid biodiversity conservation efforts

Hummingbird in flight

In today’s blog post, we’ll explore how deep learning algorithms can be harnessed to aid biodiversity conservation efforts, highlighting its potential to make a tangible difference in preserving the planet’s eco-services.

Deep Learning in Wildlife Monitoring

Deep learning, a subset of artificial intelligence, has impacted various industries with its ability to analyze vast amounts of data and extract meaningful insights. In the context of wildlife conservation, deep learning algorithms can process data from various sources, including satellite imagery, camera trap footage, and acoustic recordings, to monitor and track animal populations across different ecosystems.

One of the key applications of deep learning in wildlife monitoring is species identification and classification. By training convolutional neural networks (CNNs) on labeled images of animals, researchers can develop highly accurate models capable of automatically identifying species in photographs or videos. These models can then be deployed to analyze large datasets of wildlife imagery or footage, enabling researchers to estimate population sizes, track movements and detect rare or elusive species more efficiently than traditional methods.

Protecting Endangered Tigers

To illustrate this, let’s consider one study that was focused on protecting threatened tiger populations in their natural habitats. In regions where tigers are under threat from poaching and habitat loss, conservation organizations often deploy camera traps to monitor tiger movements and behavior. However, manually reviewing the vast amounts of photos captured by these camera traps is time-consuming and resource-intensive.

To address this challenge, the research team developed deep learning algorithms capable of automatically identifying and classifying tigers in camera trap images. By training CNNs on a dataset of labeled tiger images, these algorithms could accurately recognize individual tigers in photographs, allowing conservationists to track their movements and estimate population sizes more efficiently.

Additionally, deep learning models can be trained to detect potential threats to tigers and other endangered species, such as illegal logging or human encroachment into protected areas. By analyzing environmental data from satellite imagery or acoustic recordings, these models can alert conservationists to suspicious activities in real-time, enabling rapid response efforts to protect wildlife.

Dynamic Pollinator Network Modeling

AI algorithms can also be used to model the interactions between pollinators, flowering plants and their environment within complex ecological networks. These models can simulate how climate variability influences the timing of flowering and pollinator activity, as well as the availability of resources such as nectar and pollen.

By incorporating climate data into dynamic pollinator network models, researchers can predict how changes in temperature, precipitation and other climate-related cues affect pollinator behavior, foraging patterns, and reproductive success. These predictive models can help identify critical thresholds where climate change may disrupt pollinator communities and ecosystem functioning. 

For example, flies, bumblebees and hummingbirds were the most frequent pollinators in the network examined in this study carried out in the Andes, where entomophily (insect pollination) and anemophily (wind pollination) were the prevailing pollination syndromes. The study found that plant communities and their plant-pollination networks could be particularly vulnerable to species loss in climate change scenarios, one reason being that many species were endemic (only found in that area).

Conservationists can use this information to develop targeted interventions to support pollinator resilience and adaptation by integrating climate data with species distribution models and network simulations. One positive form of consequent human-environmental interaction from this could be that scientists can, after predicting range shifts and habitat suitability changes for endemic pollinators, help to identify new areas where conservation efforts should be prioritized.

Are you an AI or Data Scientist? is always open to contact from skilled AI scientists, researchers and computer scientists interested in putting their knowledge towards creating a cooler, cleaner Earth. Get in touch if you would like to dedicate your thesis work or internship towards a sustainable, future-friendly area of human-environmental interaction.

Key terms when understanding human-environmental interaction:

  1. Connectance Values: Connectance refers to how connected or linked different parts of a system are to each other. In the context of ecosystems or networks, connectance values measure the proportion of all possible connections that actually exist. So, if you have a network of different species (like plants and pollinators), connectance values tell you how many actual interactions there are compared to all the possible interactions that could happen. Higher connectance values mean more connections or interactions, while lower connectance values mean fewer connections.

  2. Significant Specialization: Significant specialization refers to how much a particular species relies on a specific resource or interacts with a specific partner. In ecology, species are considered specialized if they have adaptations or behaviors that make them highly dependent on certain conditions or relationships. For example, a plant species might be significantly specialized if it relies on only one type of pollinator to reproduce. Significant specialization often indicates that a species has evolved to fit a specific niche in its environment.

  3. Nestedness: Nestedness refers to how ordered or structured the interactions are within a network. In ecological networks, like those between plants and pollinators, nestedness measures how certain species interact with subsets of the species that other species interact with. Low nestedness means that the interactions between species are less ordered or structured. In other words, species are not as tightly connected in a hierarchical manner. This could mean that some species have more random or varied interactions, rather than following a clear pattern of interactions with other species.

  4. Range Shifts: In ecology, the “range” of a species refers to the geographical area where it is found. A range shift occurs when the area where a species lives changes over time, either expanding or contracting. This change in range is often driven by factors like climate change, habitat loss, or human activity.

    • Expanding Range: If a species starts to move into new areas where it wasn’t previously found, it’s called an expanding range. This could happen if the climate in those areas becomes more suitable for the species, or if humans introduce the species to new environments.

    • Contracting Range: On the other hand, if a species starts to disappear from areas where it was once common, it’s called a contracting range. This could happen if the climate becomes less suitable for the species, or if its habitat is destroyed or fragmented.

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artificial intelligence,biodiversity,conservation efforts,deep learning,pollinator-plant interaction,pollinators
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