Evaluating new technology for biodiversity monitoring: Are drone surveys biased?

Airborne imagery does not preclude detectability issues in estimating bird colony size

Aerial images obtained by drones are increasingly used for ecological research such as wildlife monitoring. Yet detectability issues resulting from animal activity or visibility are rarely considered, although these may lead to biased population size and trend estimates. In this study, we investigated detectability in a census of Malagasy pond heron Ardeola idae colonies on the island of Mayotte. We conducted repeated drone flights over breeding colonies in mangrove habitats during two breeding seasons. We then identified individuals and nests in the images and fitted closed capture-recapture models on nest-detection histories. We observed seasonal variation in the relative abundance of individuals, and intra-daily variation in the relative abundance of individuals-especially immature birds-affecting the availability of nests for detection. The detection probability of nests estimated by capture-recapture varied between 0.58 and 0.74 depending on flyover days and decreased 25% from early to late morning. A simulation showed that three flyovers are necessary to detect a 5-6% decline in colonies of 50 to 200 nests. These results indicate that the detectability of nests of forest-canopy breeding species from airborne imagery can vary over space and time; we recommend the use of capture-recapture methods to control for this bias.

Every flight is a surprise: first records of the southern maned three-toed sloth (Bradypus crinitus: Bradypodidae) through drones

Drones (unmanned aerial vehicles, UAVs), are an advanced technology that allows the collection of large amounts of data in a short amount of time, including the detection of cryptic and arboreal animals. Here, we report the first records of the southern maned sloth Bradypus crinitus obtained with thermal cameras from a drone. As far as we know, this is the first time a sloth has been observed by a drone. We recorded four individuals of B. crinitus in the Brazilian Atlantic Forest, plus first records of Procyon cancrivorus, which were all resting in the forest canopy. The individuals’ thermal responses demonstrate that the use of drones for research on these species may be a viable option to study still unexplored aspects of their ecology or conservation.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

The behavioral responses of a nocturnal burrowing marsupial (Lasiorhinus latifrons) to drone flight

The use of drones in wildlife research and management is increasing. Recent evidence has demonstrated the impact of drones on animal behavior, but the response of nocturnal animals to drone flight remains unknown. Utilizing a lightweight commercial drone, the behavioral response of southern hairy-nosed wombats (Lasiorhinus latifrons) to drone flights was observed at Kooloola Station, Swan Reach, South Australia. All wombats flown over during both day and night flights responded behaviorally to the presence of drones. The response differed based on time of day. The most common night-time behavior elicited by drone flight was retreat, compared to stationary alertness behavior observed for daytime drone flights. The behavioral response of the wombats increased as flight altitude decreased. The marked difference of behavior between day and night indicates that this has implications for studies using drones. The behavior observed during flights was altered due to the presence of the drone, and therefore, shrewd study design is important (i.e., acclimation period to drone flight). Considering the sensory adaptations of the target species and how this may impact its behavioral response when flying at night is essential.

Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.