Early in "3 Body Problem," the new Netflix adaptation of Liu Cixin's acclaimed science-fiction trilogy, intelligent life from another corner of the universe decides that a spectacle is required to get humanity's attention. On a cloudless night, the stars brighten, then flicker on and off, as if a kid were playing with a light switch, transmitting a series of numbers. Two physicists--one high and thus mesmerized, the other terrified--watch the phenomenon from a Gothic courtyard in Oxford, England. The next day, the stoner, Saul Durand (Jovan Adepo), chalks the experience up to an elaborate hoax; the rest of the world also saw the stars twinkle in code, but the celestial blinks went undetected by Earth's most powerful telescopes. The otherworldly signal may have been a message just for Saul's companion, a nanomaterials researcher named Auggie Salazar (Eiza González) who's had a glowing countdown emblazoned across her field of vision for days.

In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences. Traditionally limited by a scarcity of species observations, these models have significantly improved in performance through the integration of larger datasets provided by citizen science initiatives. However, they still suffer from the strong class imbalance between species within these datasets, often resulting in the penalization of rare species--those most critical for conservation efforts. To tackle this issue, this study assesses the effectiveness of training deep learning models using a balanced presence-only loss function on large citizen science-based datasets. We demonstrate that this imbalance-aware loss function outperforms traditional loss functions across various datasets and tasks, particularly in accurately modeling rare species with limited observations.

Visual as well as genetic biometrics are routinely employed to identify species and individuals in biological applications. However, no attempts have been made in this domain to computationally enhance visual classification of rare classes with little image data via genetics. In this paper, we thus propose aligned visual-genetic inference spaces with the aim to implicitly encode cross-domain associations for improved performance. We demonstrate for the first time that such alignment can be achieved via deep embedding models and that the approach is directly applicable to boosting long-tailed recognition (LTR) particularly for rare species. We experimentally demonstrate the efficacy of the concept via application to microscopic imagery of 30k+ planktic foraminifer shells across 32 species when used together with independent genetic data samples. Most importantly for practitioners, we show that visual-genetic alignment can significantly benefit visual-only recognition of the rarest species. Technically, we pre-train a visual ResNet50 deep learning model using triplet loss formulations to create an initial embedding space. We re-structure this space based on genetic anchors embedded via a Sequence Graph Transform (SGT) and linked to visual data by cross-domain cosine alignment. We show that an LTR approach improves the state-of-the-art across all benchmarks and that adding our visual-genetic alignment improves per-class and particularly rare tail class benchmarks significantly further. We conclude that visual-genetic alignment can be a highly effective tool for complementing visual biological data containing rare classes. The concept proposed may serve as an important future tool for integrating genetics and imageomics towards a more complete scientific representation of taxonomic spaces and life itself. Code, weights, and data splits are published for full reproducibility.

Earth could have as many as 73,000 tree species, a new first-of-its-kind study has estimated, including some 9,200 that are yet to be discovered. Most of these undiscovered species are likely to be rare, in very low numbers and at threat from human-driven changes in land use and climate, researchers said. South America contains about 43 per cent of the world's tree species and the highest number of rare ones. The findings suggest the continent should be the focus of conservation efforts, along with global tropical and subtropical forests, which also likely harbour many rare, undiscovered species, according to researchers. The study is the outcome of a three-year international project that involved almost 150 scientists and led to the identification of approximately 40 million trees belonging to 64,000 species.

Insect monitoring is crucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be scaled-up efficiently. Deep convolutional neural networks (CNNs), provide a viable way to significantly increase the biomonitoring volumes. However, taxa abundances are typically very imbalanced and the amounts of training images for the rarest classes are simply too low for deep CNNs. As a result, the samples from the rare classes are often completely missed, while detecting them has biological importance. In this paper, we propose combining the trained deep CNN with one-class classifiers to improve the rare species identification. One-class classification models are traditionally trained with much fewer samples and they can provide a mechanism to indicate samples potentially belonging to the rare classes for human inspection. Our experiments confirm that the proposed approach may indeed support moving towards partial automation of the taxa identification task.