Search

In ecology and evolutionary biology, the synthesis and modelling of data from published literature are commonly used to generate insights and test theories across systems. However, the tasks of searching, screening, and extracting data from literature are often arduous. Researchers may manually process hundreds to thousands of articles for systematic reviews, meta-analyses, and compiling synthetic datasets. As relevant articles expand to tens or hundreds of thousands, computer-based approaches can increase the efficiency, transparency and reproducibility of literature-based research. Methods available for text mining are rapidly changing owing to developments in machine learning-based language models. We review the growing landscape of approaches, mapping them onto three broad paradigms (frequency-based approaches, traditional Natural Language Processing and deep learning-based language models). This serves as an entry point to learn foundational and cutting-edge concepts, vocabularies, and methods to foster integration of these tools into ecological and evolutionary research. We cover approaches for modelling ecological texts, generating training data, developing custom models and interacting with large language models and discuss challenges and possible solutions to implementing these methods in ecology and evolution. [ABSTRACT FROM AUTHOR]

Ecology and evolutionary biology, like other scientific fields, are experiencing an exponential growth of academic manuscripts. As domain knowledge accumulates, scientists will need new computational approaches for identifying relevant literature to read and include in formal literature reviews and meta-analyses. Importantly, these approaches can also facilitate automated, large-scale data synthesis tasks and build structured databases from the information in the texts of primary journal articles, books, grey literature, and websites. The increasing availability of digital text, computational resources, and machine-learning based language models have led to a revolution in text analysis and natural language processing (NLP) in recent years. NLP has been widely adopted across the biomedical sciences but is rarely used in ecology and evolutionary biology. Applying computational tools from text mining and NLP will increase the efficiency of data synthesis, improve the reproducibility of literature reviews, formalize analyses of research biases and knowledge gaps, and promote data-driven discovery of patterns across ecology and evolutionary biology. Here we present recent use cases from ecology and evolution, and discuss future applications, limitations and ethical issues. [ABSTRACT FROM AUTHOR]

Caves provide critical roosting habitats for bats globally, but are increasingly disturbed or destroyed by human activities such as tourism and extractive industries. In addition to degrading the habitats of cave-roosting bats, such activities often promote contact between humans and bats, which may have potential impacts on human health. Cave-roosting bats are hosts to diverse viruses, some of which emerged in humans with severe consequences (e.g., severe acute respiratory syndrome coronavirus and Marburg virus). Characterizing patterns of viral richness and sharing among bat species are therefore important first steps for understanding bat-virus dynamics and mitigating future bat-human spillover. Here we compile a database of bat-virus associations and bat species ecological traits, and investigate the importance of roosting behavior as a determinant of viral richness and viral sharing among bat species. We show that cave-roosting species do not host greater viral richness, when accounting for publication bias, diet, body mass, and geographic range size. Our global analyses, however, show that cave-roosting bats do exhibit a greater likelihood of viral sharing, especially those documented in the literature as co-roosting in the same cave. We highlight the importance of caves as critical foci for bat conservation, as well as ideal sites for longitudinal surveillance of bat-virus dynamics. [ABSTRACT FROM AUTHOR]

The effects of non-lethal parasites may be felt most strongly when hosts engage in intense, energy-demanding behaviors. One such behavior is fighting with conspecifics, which is common among territorial animals, including many beetle species. We examined the effects of parasites on the fighting ability of a saproxylic beetle, the horned passalus (Odontotaenius disjunctus, Family: Passalidae), which is host to a non-lethal nematode, Chondronema passali. We pitted pairs of randomly-chosen (but equally-weighted) beetles against each other in a small arena and determined the winner and aggression level of fights. Then we examined beetles for the presence, and severity of nematode infections. There was a non-significant tendency (p = 0.065) for the frequency of wins, losses and draws to differ between beetles with and without C. passali; non-parasitized individuals (n = 104) won 47% of their fights while those with the parasite (n = 88) won 34%, a 13% difference in wins. The number of nematodes in a beetle affected the outcome of fights between infected and uninfected individuals in an unexpected fashion: fighting ability was lowest in beetles with the lowest (p = 0.033), not highest (p = 0.266), nematode burdens. Within-fight aggression was highest when both beetles were uninfected and lowest when both were infected (p = 0.034). Collectively, these results suggest the nematode parasite, C. passali, is associated with a modest reduction in fighting ability in horned passalus beetles, consistent with the idea that parasitized beetles have lower energy available for fighting. This study adds to a small but growing body of evidence showing how parasites negatively influence fighting behavior in animals. [ABSTRACT FROM AUTHOR]