Should you eat invasive species? Lionfish ceviche is surprisingly tasty. Breakthroughs, discoveries, and DIY tips sent six days a week. "By definition, invasive species are harmful in some regard," says Jacob Barney, a professor of invasive plant ecology at Virginia Tech University. So when we eat them, he adds, "we turn that harm into something positive."

Coral reefs are on the brink of collapse, with climate change, ocean acidification, and pollution leading to a projected 70-90% loss of coral species within the next decade. Restoration efforts are crucial, but their success hinges on introducing automation to upscale efforts. We present automated deployment of coral re-seeding devices powered by artificial intelligence, computer vision, and robotics. Specifically, we perform automated substrate classification, enabling detection of areas of the seafloor suitable for coral growth, thus significantly reducing reliance on human experts and increasing the range and efficiency of restoration. Real-world testing of the algorithms on the Great Barrier Reef leads to deployment accuracy of 77.8%, sub-image patch classification of 89.1%, and real-time model inference at 5.5 frames per second. Further, we present and publicly contribute a large collection of annotated substrate image data to foster future research in this area.

Breakthroughs, discoveries, and DIY tips sent every weekday. Great hammerhead sharks (Sphyrna mokarran)--the funny looking big fish with the rectangular shaped head--have an unusual feeding habit. Unlike many other sharks, who rely on small and numerous prey, great hammerheads eat other sharks. This diet is particularly high-risk, high-reward. For example, it takes a lot of energy to hunt a blacktip shark (Carcharhinus limbatus), but if successful, the hammerhead secures a high-energy meal.

How do you find an elusive animal that most people have never even seen dead in a fish market? Matthew McDavitt, above, knows how.Melody Robbins This story was originally published by Hakai Magazine and is reproduced here as part of the Climate Desk collaboration. Peter Kyne sits down at his desk to write a eulogy for a fish he's never met. No scientist has seen signs of the critically endangered Rhynchobatus cooki, or clown wedgefish, since a dead one turned up at a fish market in 1996. Kyne, a conservation biologist at Charles Darwin University in Australia who studies wedgefish, has worked only with preserved specimens of the spotted sea creature. "This thing's dust," Kyne thinks, feeling defeated as he writes the somber news in a draft assessment of the global conservation status of wedgefish species for the International Union for Conservation of Nature. Wedgefish are a type of ray.

Ecological datasets are generally characterised by complex interactions between variables, nonlinearity, missing values, dependence in the observations and/or a continuously expanding size [1-3], especially since the recent increase in the use of remote sensing and automatic recorders [4]. A growing number of those datasets cannot be effectively processed by humans anymore and require methods that can deal with high number of variables and complex data structures [3, 5, 6]. Because of their ability to process large and complicated datasets, machine learning models are expected to become a standard framework in the analysis of ecological data [3, 7, 8]. Over the last few years, machine learning algorithms have become increasingly popular due to their high performance and flexibility [8]. In ecology, they have been successfully applied to perform various tasks such as identifying species from images or sounds [9], monitoring animal behaviour [10] or modelling species distribution [11] and new innovative studies and perspectives keep being regularly documented [3, 12]. However, implementing a machine learning model is not yet a trivial task and may seem intimidating to ecologists with no previous experience in this area. In this paper, we aim to share nine tips to help ecologists avoid some of the most common errors and incorrect practices in machine learning. We focused our tips on classification problems as a substantial number of ecological studies aim to assign data into predefined classes such as ecological states or biological entities. Some typical examples of classification include species identification through pictures [9] or sound recordings [13-15], distinction of different phenological phases in plant life cycle [16, 17], description of animal behaviour [18] and detection of disease in plants [19].

1. The popularity of Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opaque, and their relationship to classical data analysis tools remains debated. 2. Although it is often assumed that ML and DL excel primarily at making predictions, ML and DL can also be used for analytical tasks traditionally addressed with statistical models. Moreover, most recent discussions and reviews on ML focus mainly on DL, missing out on synthesizing the wealth of ML algorithms with different advantages and general principles. 3. Here, we provide a comprehensive overview of the field of ML and DL, starting by summarizing its historical developments, existing algorithm families, differences to traditional statistical tools, and universal ML principles. We then discuss why and when ML and DL models excel at prediction tasks and where they could offer alternatives to traditional statistical methods for inference, highlighting current and emerging applications for ecological problems. Finally, we summarize emerging trends such as scientific and causal ML, explainable AI, and responsible AI that may significantly impact ecological data analysis in the future. 4. We conclude that ML and DL are powerful new tools for predictive modeling and data analysis. The superior performance of ML and DL algorithms compared to statistical models can be explained by their higher flexibility and automatic data-dependent complexity optimization. However, their use for causal inference is still disputed as the focus of ML and DL methods on predictions creates challenges for the interpretation of these models. Nevertheless, we expect ML and DL to become an indispensable tool in E&E, comparable to other traditional statistical tools.

The field of animal ecology has entered the era of big data and the Internet of Things. Unprecedented amounts of data are now being collected on wildlife populations, thanks to sophisticated technology such as satellites, drones and terrestrial devices like automatic cameras and sensors placed on animals or in their surroundings. These data have become so easy to acquire and share that they have shortened distances and time requirements for researchers while minimizing the disrupting presence of humans in natural habitats. Today, a variety of AI programs are available to analyze large datasets, but they're often general in nature and ill-suited to observing the exact behavior and appearance of wild animals. A team of scientists from EPFL and other universities has outlined a pioneering approach to resolve that problem and develop more accurate models by combining advances in computer vision with the expertise of ecologists. Their findings, which appear today in Nature Communications, open up new perspectives on the use of AI to help preserve wildlife species.

When we humans imagine the future, it is common to picture ourselves nested within an ecosystem populated by robots, devices, and virtual realities. The future is shining and technological. The future is digital, ones and zeros, electricity and invisible connections. The dangers of the future--automation and artificial intelligence--are of our own invention. Nature is an afterthought in our contemplation of what comes next, a transgenic potted plant behind a window that does not open.

The American Association for the Advancement of Science (AAAS) has selected Stony Brook University Professor Heather Lynch as one of 12 researchers in the area of artificial intelligence (AI) to be a 2020-2021 AAAS Alan I. Leshner Leadership Institute Public Engagement Fellow. Lynch, Institute for Advanced Computational Science Endowed Chair for Ecology & Evolution, was chosen for having demonstrated leadership and excellence in her research career and for her interest in promoting meaningful dialogue between science and society. AI focuses on developing and studying machines and algorithms that augment or mimic human abilities, learn from and adjust to new situations, and perform tasks. As a quantitative ecologist whose research focuses on the population dynamics of Antarctic wildlife, Lynch uses AI to study penguins, among other species. "Communicating with the public about penguins is pretty easy, but explaining AI? That's quite a bit harder," said Lynch.

Machine learning could help complete research tasks usually given to citizen scientists. A new study shows how teaching a computer specific image recognition skills can be used in projects that require classification of large amounts of image data. For years scientists have taken advantage of volunteers who help them sort through massive datasets that are too large for small research teams. Previously this work was needed to be done by humans because the technology for a machine to do it didn't exist. But that is all about to change.