Human activities drive ecological transformation, impacting island ecosystems from species diversity to ecological traits, mainly through habitat degradation and invasive species. Using two unique long-term datasets we aim to evaluate whether species traits (body size, trophic level, dispersal capacity and habitat occupancy) can predict temporal variations in the abundance of endemic, native non-endemic and exotic arthropods in the Azores Islands. We found that body size is crucial to predict arthropods abundance trends. Small-bodied herbivorous arthropods showed a decrease in abundance, while large-bodied indigenous arthropods increased in abundance, mainly in well-preserved areas. Also, large-bodied exotic arthropods increased in abundance across the entire archipelago. Moreover, endemic canopy dwellers increased in abundance, while endemic ground-dwellers decreased in abundance. Simultaneously, exotic arthropods showed the opposite result, increasing abundance in the ground while decreasing abundance in the canopy. Finally, habitat influenced both endemic and exotic spider abundance trends. Endemic spiders that occupy solely natural habitats experienced a decline in abundance, while exotic spiders in the same habitats increased in abundance. Our study underscores the significance of arthropod species traits in predicting abundance changes in island ecosystems over time, as well as the importance of monitoring species communities. Conservation efforts must extend beyond endangered species to protect non-threatened ones, given the increased extinction risk faced by even common species on islands. Monitoring and restoration programs are essential for preserving island ecosystems and safeguarding endemic arthropod populations.

Oceanic islands are among the most transformed ecosystems in the world, with many having experienced major biotic changes through the combined effects of species extinctions and introductions. We map global patterns of taxonomic and functional change in 64 oceanic island bird assemblages and investigate whether these patterns can be explained by either island characteristics (geography, climate) or anthropogenic factors (human occupation, connectivity). The Hawaiian and Mascarene islands stand out as hotspots of taxonomic and functional change, but all islands changed taxonomically and functionally, mostly gaining species but losing functional richness. Island isolation and aridity can explain some of the observed variation in levels of change, but anthropogenic factors have a stronger effect. Remote humid islands have a stronger susceptibility to assemblage turnover and should be prioritized for conservation through the protection of native species and their ecosystems, and by preventing further introductions.

AbstractMachine learning is a growing computational field that borrows concepts and methodologies from statistics and data science to create semi-autonomous programmes capable of adapting to a multitude of problems and decision-making scenarios. With its potential in big data analysis, machine learning is particularly useful for tackling global conservation problems that often involve vast amounts of data and complex interactions between variables. In this systematic review, we summarise the use of machine learning methods in the study of species threats and conservation measures, and their emergent trends. Maximum entropy, Bayesian and ensemble methods have gained wide popularity in the past years and are now commonly used for multiple problems. Their relevance to modern conservation issues (and associated data types), their relatively simple implementation, and availability in a variety of software packages are the most likely factors to explain their popularity. Neural networks, decision trees, support-vector machines and evolutionary algorithms have been used in more specific situations, with some applications showing promise in dealing with increasingly complex data and scenarios.

Our current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species. This is due to inherent problems with obtaining high-quality arthropod data. Novel high throughput sequencing approaches are now emerging as powerful tools to overcome such limitations, and thus comprehensively address existing shortfalls in arthropod biodiversity data. Here we explore how, as a community, we might most effectively exploit these tools for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first review the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We consider how this can be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explore how these approaches can be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identify seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonised efforts in HTS arthropod inventory could yield significant advances in island biodiversity research.