Richard Kingsford’s research while affiliated with UNSW Sydney and other places

Introduction Drylands are a major terrestrial biome, supporting much of the earth's population. Soil microbial communities maintain drylands’ ecosystem functions but are threatened by increasing temperature. Groundcover, such as vegetation or biocrust, drives the patchiness of drylands' soil microbial communities, reflected in fertile islands and rhizosphere soil microbial associations. Groundcover may shelter soil microbial communities from increasingly harsh temperatures under climate change, mitigating effects on microclimate, but few data on the microbial response exists. Understanding the fine‐scale interactions between plants and soil is crucial to improving conservation and management of drylands under climate change. Materials and Methods We used open‐top chambers to experimentally increase the temperature on five key groundcover species found in arid Australia, and are commonly present in drylands worldwide; bareground (controls), biocrust, perennial grass, Maireana sp. shrub, Acacia aneura trees, testing soil bacterial diversity and community composition response to the effects of increased temperatures. Results We found that groundcover was a stronger driver of soil bacterial composition than increased temperature, but this response varied with groundcover type. Larger groundcover types (Acacia and Maireana) buffered the impact of heat stress on the soil bacterial community. Bacterial diversity and species richness declined with heat stress affecting the bacterial communities associated with perennial grass, Maireana and Acacia. We identified 16 bacterial phyla significantly associated with groundcover types in ambient treatment. But, under heat stress, only three phyla, Verrumicrobiota, Patescibacteria, and Abditibacteriota, had significantly different relative abundance under groundcovers, Acacia and Maireana, compared to bareground controls. The soil bacterial community associated with perennial grass was most affected by increased temperature. Conclusion Our findings suggest soil communities may become more homogeneous under climate change, with compositional change, rather than diversity, tracking soil response to heat stress.

1. Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count classify animals and their behaviours. Yet, we currently lack a systematic literature survey on its use in wildlife imagery.2. Through a literature survey (a ‘rapid’ review) and bibliometric mapping, we explored its use across: 1) species (vertebrates), 2) image types (e.g., camera traps, or drones), 3) study locations, 4) alternative machine learning algorithms, 5) outcomes (e.g., recognition, classification, or tracking), 6) reporting quality and openness, 7) author affiliation, and 8) publication journal types.3. Typically, studies have focused on single large charismatic or iconic mammalian species and used neural networks (i.e., deep learning). Additional taxa or alternative machine learning algorithms were rarely used, with limited sharing of code. There were considerable gaps, and therefore there is a great promise for deep learning to transform behavioural detection, classification, and tracking of wildlife.4. Much of the published research and focus on animals came from India, China, Australia, or the USA. There were relatively few collaborations across countries. Given the power of machine learning, we recommend increasing collaboration and sharing approaches to utilise increasing amounts of wildlife imagery more rapidly and transform and improve understanding of wildlife behaviour and conservation.5. Our survey augmented with bibliometric analyses provide valuable signposts for future studies to resolve and address shortcomings, gaps, and biases.