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Pollen identification is necessary for several subfields of geology, ecology, and evolutionary biology. However, the existing methods for pollen identification are laborious, time-consuming, and require highly skilled scientists. Therefore, there is a pressing need for an automated and accurate system for pollen identification, which can be beneficial for both basic research and applied issues such as identifying airborne allergens. In this study, we propose a deep learning (DL) approach to classify pollen grains in the Great Basin Desert, Nevada, USA. Our dataset consisted of 10,000 images of 40 pollen species. To mitigate the limitations imposed by the small volume of our training dataset, we conducted an in-depth comparative analysis of numerous pre-trained Convolutional Neural Network (CNN) architectures utilizing transfer learning methodologies. Simultaneously, we developed and incorporated an innovative CNN model, serving to augment our exploration and optimization of data modeling strategies. We applied different architectures of well-known pre-trained deep CNN models, including AlexNet, VGG-16, MobileNet-V2, ResNet (18, 34, and 50, 101), ResNeSt (50, 101), SE-ResNeXt, and Vision Transformer (ViT), to uncover the most promising modeling approach for the classification of pollen grains in the Great Basin. To evaluate the performance of the pre-trained deep CNN models, we measured accuracy, precision, F1-Score, and recall. Our results showed that the ResNeSt-110 model achieved the best performance, with an accuracy of 97.24%, precision of 97.89%, F1-Score of 96.86%, and recall of 97.13%. Our results also revealed that transfer learning models can deliver better and faster image classification results compared to traditional CNN models built from scratch. The proposed method can potentially benefit various fields that rely on efficient pollen identification. This study demonstrates that DL approaches can improve the accuracy and efficiency of pollen identification, and it provides a foundation for further research in the field.

 

Humans did not arrive on most of the world’s islands until relatively recently, making islands favorable places for disentangling the timing and magnitude of natural and anthropogenic impacts on species diversity and distributions. Here, we focus onAmazonaparrots in the Caribbean, which have close relationships with humans (e.g., as pets as well as sources of meat and colorful feathers). Caribbean parrots also have substantial fossil and archaeological records that span the Holocene. We leverage this exemplary record to showcase how combining ancient and modern DNA, along with radiometric dating, can shed light on diversification and extinction dynamics and answer long-standing questions about the magnitude of human impacts in the region. Our results reveal a striking loss of parrot diversity, much of which took place during human occupation of the islands. The most widespread species, the Cuban Parrot, exhibits interisland divergences throughout the Pleistocene. Within this radiation, we identified an extinct, genetically distinct lineage that survived on the Turks and Caicos until Indigenous human settlement of the islands. We also found that the narrowly distributed Hispaniolan Parrot had a natural range that once included The Bahamas; it thus became “endemic” to Hispaniola during the late Holocene. The Hispaniolan Parrot also likely was introduced by Indigenous people to Grand Turk and Montserrat, two islands where it is now also extirpated. Our research demonstrates that genetic information spanning paleontological, archaeological, and modern contexts is essential to understand the role of humans in altering the diversity and distribution of biota.

 

Classification of the biological diversity on Earth is foundational to all areas of research within the natural sciences. Reliable biological nomenclatural and taxonomic systems facilitate efficient access to information about organisms and their names over time. However, broadly sharing, accessing, delivering, and updating these resources remains a persistent problem. This barrier has been acknowledged by the biodiversity data sharing community, yet concrete efforts to standardize and continually update taxonomic names in a sustainable way remain limited. High diversity groups such as arthropods are especially challenging as available specimen data per number of species is substantially lower than vertebrate or plant groups. The Terrestrial Parasite Tracker Thematic Collections Network project developed a workflow for gathering expert-verified taxonomic names across all available sources, aligning those sources, and publishing a single resource that provides a model for future endeavors to standardize digital specimen identification data. The process involved gathering expert-verified nomenclature lists representing the full taxonomic scope of terrestrial arthropod parasites, documenting issues experienced, and finding potential solutions for reconciliation of taxonomic resources against large data publishers. Although discordance between our expert resources and the Global Biodiversity Information Facility are relatively low, the impact across all taxa affects thousands of names that correspond to hundreds of thousands of specimen records. Here, we demonstrate a mechanism for the delivery and continued maintenance of these taxonomic resources, while highlighting the current state of taxon name curation for biodiversity data sharing.

 

Research on plant-pollinator interactions requires a diversity of perspectives and approaches, and documenting changing pollinator-plant interactions due to declining insect diversity and climate change is especially challenging. Natural history collections are increasingly important for such research and can provide ecological information across broad spatial and temporal scales. Here, we describe novel approaches that integrate museum specimens from insect and plant collections with field observations to quantify pollen networks over large spatial and temporal gradients. We present methodological strategies for evaluating insect-pollen network parameters based on pollen collected from museum insect specimens. These methods provide insight into spatial and temporal variation in pollen-insect interactions and complement other approaches to studying pollination, such as pollinator observation networks and flower enclosure experiments. We present example data from butterfly pollen networks over the past century in the Great Basin Desert and Sierra Nevada Mountains, United States. Complementary to these approaches, we describe rapid pollen identification methods that can increase speed and accuracy of taxonomic determinations, using pollen grains collected from herbarium specimens. As an example, we describe a convolutional neural network (CNN) to automate identification of pollen. We extracted images of pollen grains from 21 common species from herbarium specimens at the University of Nevada Reno (RENO). The CNN model achieved exceptional accuracy of identification, with a correct classification rate of 98.8%. These and similar approaches can transform the way we estimate pollination network parameters and greatly change inferences from existing networks, which have exploded over the past few decades. These techniques also allow us to address critical ecological questions related to mutualistic networks, community ecology, and conservation biology. Museum collections remain a bountiful source of data for biodiversity science and understanding global change. 

A new avian chewing louse genus Apomyrsidea gen. nov. is described based on species parasitizing birds in the family Formicariidae. Diagnostic characteristics and phylogenetic analyses were used to evaluate and confirm the generic status and merit its recognition as unique and different from Myrsidea Waterston, 1915. Three species previously belonging to the genus Myrsidea are placed in the new genus Apomyrsidea gen. nov. and are discussed: Apomyrsidea circumsternata (Valim & Weckstein, 2013) gen. et comb. nov., Apomyrsidea isacantha (Valim & Weckstein, 2013) gen. et comb. nov. and Apomyrsidea klimesi (Sychra in Sychra et al., 2006) gen. et comb. nov. 

Adaptive radiation is an important mechanism of organismal diversification and can be triggered by new ecological opportunities. Although poorly studied in this regard, parasites are an ideal group in which to study adaptive radiations because of their close associations with host species. Both experimental and comparative studies suggest that the ectoparasitic wing lice of pigeons and doves have adaptively radiated, leading to differences in body size and overall coloration. Here, we show that long-distance dispersal by dove hosts was central to parasite diversification because it provided new ecological opportunities for parasites to speciate after host-switching. We further show that among extant parasite lineages host-switching decreased over time, with cospeciation becoming the more dominant mode of parasite speciation. Taken together, our results suggest that host dispersal, followed by host-switching, provided novel ecological opportunities that facilitated adaptive radiation by parasites. 

Batesian mimicry (BM), where a nontoxic species resembles a toxic species with aposematic coloring, has been recently described for a Neotropical species of the suboscine passerine (Laniocera hypopyrra). Understanding the order and series in which these characteristics evolved is unknown and requires character information from closely related taxa. Here, we trace the origin of mimetic traits and how they evolved by examining antipredator characteristics using images and other field‐collected trait data from nest and nestlings along with data available in the literature for the Laniisominae clade and closely related taxa. We found that morphological modifications of the downy feathers appeared first in the broader clade leading to the Laniisominae clade followed by further morphological and behavioral characteristics within the Laniisominae clade leading to the full BM. Images of nestlings in the Laniisominae and closely related clades demonstrated the extent of antipredator and camouflage characteristics. We found a complex set of behavioral and morphological traits in this clade for reducing predation from hiding to camouflage to mimicry. We further propose the evolution of two distinctive mimicry strategies in the Laniisominae clade: (1) Batesian Mimicry, as described above and (2) Masquerade, resemblance to inedible objects commonly found in their local environment. This complex set of antipredator traits shed light on the diversity of antipredator characteristics in avian nestlings, particularly in neotropical areas where the avian diversity is highest. Unfortunately, there are hundreds of species in the neotropics that lack basic natural history information on nesting traits, and therefore, we are likely missing critical information on the diversity of antipredator characteristics across avian nestlings.