The noninvasive method is already monitoring over 100 Alaskan brown bears. Breakthroughs, discoveries, and DIY tips sent six days a week. Instead, the desire to survive generally wins out over lingering to admire the predator's sizable claws or snout shape. Knowing this, you'd be forgiven for having difficulty differentiating one bear from another. For many ecologists, monitoring individual animals over long periods of time--even years--is crucial to conservation efforts.

Scientists listened to these Caribbean fish for over 2,000 hours. Breakthroughs, discoveries, and DIY tips sent every weekday. The red hind grouper () is big on grunting. After analyzing over 2,000 hours of ocean acoustic recordings gathered over 12 years, marine biologists say that groupers convey specific messages to one another about courtship and territory with their grunts. And with the help of an advanced machine-learning tool, researchers now believe the observational approach detailed in a study published in the can help other scientists to better monitor fish populations, as well as improve ongoing conservation efforts for threatened species.

Understanding and monitoring aquatic biodiversity is critical for ecological health and conservation efforts. This paper proposes SuoiAI, an end-to-end pipeline for building a dataset of aquatic invertebrates in Vietnam and employing machine learning (ML) techniques for species classification. We outline the methods for data collection, annotation, and model training, focusing on reducing annotation effort through semi-supervised learning and leveraging state-of-the-art object detection and classification models. Our approach aims to overcome challenges such as data scarcity, fine-grained classification, and deployment in diverse environmental conditions.

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real - time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real - time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno - canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel - Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real - world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation.

The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes this data with convolutional neural networks (CNNs) and Transformer architectures to monitor species, including those which are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g. poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform's success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints, while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.

We've been wondering what goes on inside the minds of animals since antiquity. Dr. Doolittle's talent was far from novel when it was first published in 1920; Greco-Roman literature is lousy with speaking animals, writers in Zhanguo-era China routinely ascribed language to certain animal species and they're also prevalent in Indian, Egyptian, Hebrew and Native American storytelling traditions. The dolphins from both Seaquest DSV and Johnny Mnemonic communicated with their bipedal contemporaries through advanced translation devices, as did Dug the dog from Up. We've already got machine-learning systems and natural language processors that can translate human speech into any number of existing languages, and adapting that process to convert animal calls into human-interpretable signals doesn't seem that big of a stretch. However, it turns out we've got more work to do before we can converse with nature. "All living things communicate," an interdisciplinary team of researchers argued in 2018's On understanding the nature and evolution of social cognition: a need for the study of communication.

Facial recognition technology is mostly associated with uses such as surveillance and the authentication of human faces, but scientists believe they've found a new use for it -- saving seals. A research team at Colgate University has developed SealNet, a database of seal faces created by taking pictures of dozens of harbor seals in Maine's Casco Bay. The team found the tool's accuracy in identifying the marine mammals is close to 100%, which is no small accomplishment in an ecosystem home to thousands of seals. The researchers are working on expanding their database to make it available to other scientists, said Krista Ingram, a biology professor at Colgate and a team member. Broadening the database to include rare species such as the Mediterranean monk seal and Hawaiian monk seal could help inform conservation efforts to save those species, she said.

In 2017, a group of scientists were struck by a startling realization – sperm whale vocalizations, that sound like clicks, resemble Morse Code to a great extent. It sowed the seeds for an ambitious project -- the Cetacean Translation Initiative, or Project CETI -- that would use artificial intelligence to translate these whale sounds such that humans would be able to understand them. The introduction of tech into studying animal behavior not only helps us understand them better -- but also, paradoxically, helps reveal our own limits as a species. This could go one of two ways: enable greater conservation efforts, or instil a hubris that could use the newfound knowledge of animal communication against them. It is not just whale communication that has been the subject of translation initiatives.

For over hundreds of millions of years, sea turtles and their ancestors have swum in the vast expanses of the ocean. They have undergone a number of evolutionary changes, leading to speciation and sub-speciation. However, in the past few decades, some of the most notable forces driving the genetic variance and population decline have been global warming and anthropogenic impact ranging from large-scale poaching, collecting turtle eggs for food, besides dumping trash including plastic waste into the ocean. This leads to severe detrimental effects in the sea turtle population, driving them to extinction. This research focusses on the forces causing the decline in sea turtle population, the necessity for the global conservation efforts along with its successes and failures, followed by an in-depth analysis of the modern advances in detection and recognition of sea turtles, involving Machine Learning and Computer Vision systems, aiding the conservation efforts.

Did you know that scientists have identified only 1.5 million species out of the 10 million estimated on Earth? And many of those species are vulnerable to extinction. Thanks to the efforts of the non-profit organization Wild Me, the gargantuan task of wildlife preservation is getting a much-needed assist from citizen scientists who photograph and video wildlife when traveling the world, plus high-tech solutions such as cloud computing, artificial intelligence, and machine vision. The Amazing Ways Wild Me Uses Artificial Intelligence And Citizen Scientists To Help With ... [ ] Conservation To make the progress on wildlife conservation that's necessary, it's going to take pulling data out of proprietary data sets and joining them into collaborative data sets. This is precisely what Wild Me and its Wildbook platform can do for the effort.