Mortality assessment of moose (Alces alces) calves during successive years of winter tick (Dermacentor albipictus) epizootics in New Hampshire and Maine (USA)

Populations within ecological communities constantly fluctuate due to a multitude of interactions that can be influenced by climate change. Moose (Alces alces (Linnaeus, 1758)) populations in northern New Hampshire and western Maine, subunits of the largest regional moose population in the continental United States, are suspected to be declining due to increasing frequency of winter tick (Dermacentor albipictus Packard, 1869) epizootics that cause >50% late-winter mortality of 9- to 12-month-old calves. To investigate this hypothesis, we collected general health measurements of calves captured at two study sites in January 2014–2016 and subsequently performed field necropsies and histologic examination of tissues of those radio-marked calves that died during winter and spring. At capture, calves (n = 179) were in normal (66%) and thin (32%) physical condition with high infestations of winter ticks. Most (88%) mortalities (n = 125) were associated with moderate to severe infestations of winter ticks. Gross necropsies and histologic examination found high tick infestations, emaciation, anemia, and endoparasitism; lungworm (species of the genus Dictyocaulus Railliet and Henry, 1907) was also found in most (87%) calves. Three consecutive years (2014–2016) of winter tick epizootics is unprecedented in the region, rare in North America, and arguably reflects a host–parasite relationship strongly influenced by climate change at the southern fringe of moose habitat.

Clair AL, Dolezal A, Toth AL, O’Neal M. Honey Bee (Hymenoptera: Apidea) Pollen Forage in a Highly Cultivated Agroecosystem: Limited Diet Diversity and Its Relationship to Virus Resistance

Intensified agriculture reduces natural and seminatural habitats and plant diversity, reducing forage available to honey bees (Apis mellifera L. [Hymenoptera: Apidea]). In agricultural landscapes of Iowa, United States, we studied the impact of extrinsic agricultural intensification on the availability of pollen for honey bees by placing colonies next to soybean fields surrounded by either a low or high level of cultivation. The abundance and diversity of pollen returned to a colony were estimated by placing pollen traps on bee colonies during the summer and fall of 2015 and 2016. We observed no difference in abundance and diversity of pollen collected by colonies in either landscape, but abundance varied over time with significantly less collected in September. We explored if the most commonly collected pollen from these landscapes had the capacity to support honey bee immune health by testing if diets consisting of these pollens improved bee resistance to a viral infection. Compared to bees denied pollen, a mixture of pollen from the two most common plant taxa (Trifolium spp. L. [Fabales: Fabaceae] and Chimaechrista fasciculata (Michx.) Greene [Fabales: Fabaceae]) significantly reduced honey bee mortality induced by viral infection. These data suggest that a community of a few common plants was favored by honey bees, and when available, could be valuable for reducing mortality from a viral infection. Our data suggest a late season shortage of pollen may be ameliorated by additions of fall flowering plants, like goldenrod (Solidago spp. L. [Asterales: Asteraceae]) and sunflower (Helianthus, Heliopsis, and Silphium spp. [Asterales: Asteraceae]), as options for enhancing pollen availability and quality for honey bees in agricultural landscapes.

InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline

Insect pests significantly impact global agricultural productivity and crop quality. Effective integrated pest management strategies require the identification of insects, including beneficial and harmful insects. Automated identification of insects under real-world conditions presents several challenges, including the need to handle intraspecies dissimilarity and interspecies similarity, life-cycle stages, camouflage, diverse imaging conditions, and variability in insect orientation. An end-to-end approach for training deep-learning models, InsectNet, is proposed to address these challenges. Our approach has the following key features: (i) uses a large dataset of insect images collected through citizen science along with label-free self-supervised learning to train a global model, (ii) fine-tuning this global model using smaller, expert-verified regional datasets to create a local insect identification model, (iii) which provides high prediction accuracy even for species with small sample sizes, (iv) is designed to enhance model trustworthiness, and (v) democratizes access through streamlined machine learning operations. This global-to-local model strategy offers a more scalable and economically viable solution for implementing advanced insect identification systems across diverse agricultural ecosystems. We report accurate identification (>96% accuracy) of numerous agriculturally and ecologically relevant insect species, including pollinators, parasitoids, predators, and harmful insects. InsectNet provides fine-grained insect species identification, works effectively in challenging backgrounds, and avoids making predictions when uncertain, increasing its utility and trustworthiness. The model and associated workflows are available through a web-based portal accessible through a computer or mobile device. We envision InsectNet to complement existing approaches, and be part of a growing suite of AI technologies for addressing agricultural challenges.