Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduces a new automated method for detection using published object detection algorithms to detect their heat signatures in RPAS-derived thermal imaging. As an initial case study we used this new approach to detect koalas (Phascolarctus cinereus), and validated the approach using ground surveys of tracked radio-collared koalas in Petrie, Queensland. The automated method yielded a higher probability of detection (68–100%), higher precision (43–71%), lower root mean square error (RMSE), and lower mean absolute error (MAE) than manual assessment of the RPAS-derived thermal imagery in a comparable amount of time. This new approach allows for more reliable, less invasive detection of koalas in their natural habitat. This new detection methodology has great potential to inform and improve management decisions for threatened species, and other difficult to survey species.

Drones and machine learning‐based automated detection methods are being used by ecologists to conduct wildlife surveys with increasing frequency. When traditional survey methods have been evaluated, a range of factors have been found to influence detection probabilities, including individual differences among conspecific animals, which can thus introduce biases into survey counts. There has been no such evaluation of drone‐based surveys using automated detection in a natural setting. This is important to establish since any biases in counts made using these methods will need to be accounted for, to provide accurate data and improve decision‐making for threatened species. In this study, a rare opportunity to survey a ground‐truthed, individually marked population of 48 koalas in their natural habitat allowed for direct comparison of the factors impacting detection probability in both ground observation and drone surveys with manual and automated detection. We found that sex and host tree preferences impacted detection in ground surveys and in manual analysis of drone imagery with female koalas likely to be under‐represented, and koalas higher in taller trees detected less frequently when present. Tree species composition of a forest stand also impacted on detections. In contrast, none of these factors impacted on automated detection. This suggests that the combination of drone‐captured imagery and machine learning does not suffer from the same biases that affect conventional ground surveys. This provides further evidence that drones and machine learning are promising tools for gathering reliable detection data to better inform the management of threatened populations.

Identifying and implementing management actions that can mitigate the impacts of climate change on domestically grown crops is crucial to maintaining future food security for the United Kingdom (UK). Crop models serve as critical tools for assessing the potential impacts of climate change and making decisions regarding crop management. However, there is often a gap between yields predicted by current modeling methods and observed yields. This has been linked to a sparsity of models that investigate crop yield beyond field scale or that include data on crop management or crop protection factors. It remains unclear whether the lack of available data imposes these limitations or if the currently available data presents untapped opportunities to extend models to better capture the complex ecosystem of factors affecting crop yield. In this paper, we synthesize available data on plant physiology, management, and protection practices for agricultural crops in the UK, as well as associated data on climate and soil conditions. We then compare the available data to the variables used to predict crop yield using current modeling methods. We find there is a lack of openly accessible crop management and crop plant physiology data, particularly for crops other than wheat, which could limit improvements in current crop models. Conversely, data that was found to be available at large scales on climate and soil conditions could be used to explore upscaling of current approaches beyond the field level, and available data on crop protection factors could be integrated into existing models to better account for how disease, insect pest and weed pressures may impact crop yield under different climate scenarios. We conclude that while a lack of available data on crop management, protection, physiology, at scales other than field level, and for species other than wheat currently hampers advancement of modeling methods for UK crops, future investment into data collection and management across a broader range of factors affecting crops, at larger scales and for a broader range of crop species could improve predictions of crop plant development and yield.

Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N‐mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies. We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N‐mixture model and (b) a modified Horvitz–Thompson (H‐T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry‐assisted ground surveys. The modified H‐T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods. The results suggested that N‐mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections.