Invasive species are one of the greatest threats to biodiversity, food security, and ecosystem health. When non-native plants spread into new environments, they can outcompete native species, disrupt habitats, and cause long-lasting damage to the natural habitats and the services that ecosystems provide to humans. Continue reading Using machine learning to predict invasion success
Las especies invasoras representan una de las mayores amenazas para la biodiversidad, la seguridad alimentaria y la salud de los ecosistemas. Cuando las plantas no-nativas se propagan en nuevos ambientes, pueden desplazar a las especies nativas, alterar los hábitats y causar daños severos a los ecosistemas y a los servicios ecosistémicos que estos brindan a los seres humanos. Continue reading Inteligencia artificial para predecir el éxito de las invasiones biológicas
Emily A. Jordan discusses the use of AI in population monitoring and her team’s experience using it to assess the Kapitia skink. In population monitoring, using unique markings to identify individuals is a practical solution when species are challenging to tag. We can camera trap elusive snow leopards, drone-photograph whales, and happily snap our tiniest amphibians. Yet these photographic records bring a fresh challenge. Each … Continue reading AI and population monitoring; does it really make a difference?
The British Ecological Society journals Ecological Solutions and Evidence and Methods in Ecology and Evolution are seeking proposals for its new cross-journal Special Feature: “Innovation in Practice“. Applied ecological management relies in part on the application of technology to help mitigate anthropogenic impacts and facilitate the recovery of populations and ecosystems. In the past few decades, new and advanced technology has been applied to solve … Continue reading Call for proposals: Innovation in Practice
In their new study, Searle, Kaszta, and co-authors from Botswana, Zimbabwe, Germany, the UK, and the US discuss how machine learning can be used to disentangle multi-species habitat relationships and inform conservation planning over large areas. The importance of policy and governance in preserving wildlife areas has historically meant that conservation has been restricted to efforts within country borders. This approach is at odds with … Continue reading Old data, new tools: Using random forest modelling to reveal multi-species habitat associations from spoor data
Buff-tailed bumblebee (Bombus terrestris) feeding on the nectar of Creeping thistle (Cirsium arvense) flowers © Damien Hicks Authors Damien Hicks and Christoph Kratz introduce their team’s latest research demonstrating the use of machine learning for quadrat surveys to improve accessibility and resource efficiency of current methods for floral vegetation monitoring. The nectar sugar contained in flowers is a key driver of pollinator abundance and diversity. … Continue reading A deep learning model for pollinator plant surveys
The Applied Ecologist 