Enhancing the effectiveness of biological control programs of invasive species through a more comprehensive pest management approach

Incorporating Microbial Species Interaction in Management of Freshwater Toxic Cyanobacteria: A Systems Science Challenge

Water resources are critically important, but also pose risks of exposure to toxic and pathogenic microbes. Increasingly, a concern is toxic cyanobacteria, which have been linked to the death and disease of humans, domesticated animals, and wildlife in freshwater systems worldwide. Management approaches successful at reducing cyanobacterial abundance and toxin production have tended to be short-term solutions applied on small scales (e.g., algaecide application) or solutions that entail difficult multifaceted investments (e.g., modification of landscape and land use to reduce nutrient inputs). However, implementation of these approaches can be undermined by microbial species interactions that (a) provide toxic cyanobacteria with protection against the method of control or (b) permit toxic cyanobacteria to be replaced by other significant microbial threats. Understanding these interactions is necessary to avoid such scenarios and can provide a framework for novel strategies to enhance freshwater resource management via systems science (e.g., pairing existing physical and chemical approaches against cyanobacteria with ecological strategies such as manipulation of natural enemies, targeting of facilitators, and reduction of benthic occupancy and recruitment). Here, we review pertinent examples of the interactions and highlight potential applications of what is known.

Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response

The ability of deep convolutional neural networks (deep learning) to learn complex visual characteristics offers a new method to classify tree species using lower-cost data such as regional aerial RGB imagery. In this study, we use 10 cm resolution imagery and 4600 trees to develop a deep learning model to identify Metrosideros excelsa (pōhutukawa)—a culturally important New Zealand tree that displays distinctive red flowers during summer and is under threat from the invasive pathogen Austropuccinia psidii (myrtle rust). Our objectives were to compare the accuracy of deep learning models that could learn the distinctive visual characteristics of the canopies with tree-based models (XGBoost) that used spectral and textural metrics. We tested whether the phenology of pōhutukawa could be used to enhance classification by using multitemporal aerial imagery that showed the same trees with and without widespread flowering. The XGBoost model achieved an accuracy of 86.7% on the dataset with strong phenology (flowering). Without phenology, the accuracy fell to 79.4% and the model relied on the blueish hue and texture of the canopies. The deep learning model achieved 97.4% accuracy with 96.5% sensitivity and 98.3% specificity when leveraging phenology—even though the intensity of flowering varied substantially. Without strong phenology, the accuracy of the deep learning model remained high at 92.7% with sensitivity of 91.2% and specificity of 94.3% despite significant variation in the appearance of non-flowering pōhutukawa. Pooling time-series imagery did not enhance either approach. The accuracy of XGBoost and deep learning models were, respectively, 83.2% and 95.2%, which were of intermediate precision between the separate models.