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Sucking lice are obligate parasites of eutherian mammals and are generally considered to be host-specific parasites. Molecular investigations have found that some current louse taxonomy is incorrect and does not reflect the relationships among families and species. Western chipmunks (23 species of Tamias) and the eastern chipmunk (Tamias striatus) are infested by 2 different species of Hoplopleura sucking lice, Hoplopleura arboricola and Hoplopleura erratica. Hoplopleura arboricola has been found on 19 of 23 western chipmunk species, and H. erratica has only been recorded as a parasite of T. striatus. We investigated the relationships between these chipmunk lice and louse systematic status by supplementing published sequence data with additional sequences and morphological examinations. We estimated phylogenetic relationships using 1,107 coding loci in a maximum-likelihood framework and a species tree approach. In addition to the phylogeny, we calculated raw pairwise distances of the cytochrome oxidase subunit 1 gene (COI) between clades. Both phylogenetic approaches recovered 2 well-supported clades of H. arboricola, 1 of which included H. erratica, suggesting that the 2 louse species are not distinct. Further, examination of louse specimens found no morphological traits that distinguish lice from any of the lineages, including differentiating H. erratica from H. arboricola. The average pairwise distance of COI sequences between the 2 major H. arboricola clades exceeded that of the distances between H. erratica and either of the H. arboricola clades. Based on the genetic similarities and phylogenetic relationships of the lice, it appears that an ancestral louse was associated with western chipmunks and then transferred to the eastern chipmunk. Using the phylogenetic and morphological evidence presented here, Hoplopleura arboricolaKellogg and Ferris, 1915 is relegated to a junior subjective synonym of Hoplopleura erratica (Osborn, 1896). A holotype from the type series is designated for H. erratica. These results suggest a history of chipmunk host species interactions that enabled ectoparasites to disperse between chipmunk species and illustrate the importance of phylogenomic analyses to study species interactions and the history of interspecific associations. 

Abstract PremiseOne of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields. MethodsWe first showcase the development of a rule‐based approach and compare outcomes with a large language model–based approach, in particular ChatGPT4. We next quantified omission and commission error rates across target fields for a set of labels transcribed using optical character recognition (OCR) for both approaches. For example, we find that ChatGPT4 often creates field names that are not Darwin Core compliant while rule‐based approaches often have high commission error rates. ResultsOur results suggest that these approaches each have different strengths and limitations. We therefore developed an ensemble approach that leverages the strengths of each individual method and documented that ensembling strongly reduced overall information extraction errors. DiscussionThis work shows that an ensemble approach has particular value for creating high‐quality digital data records, even for complicated label content. While human validation is still needed to ensure the best possible quality, automated approaches can speed digitization of herbarium specimen labels and are likely to be broadly usable for all natural history collection types. 

Islands are well known for their unique biodiversity and significance in evolutionary and ecological studies. Nevertheless, the extinction of island species accounts for most human-caused extinctions in recent time scales, which have accelerated in recent centuries. Pigeons and doves (Columbidae) are noteworthy for the high number of island endemics, as well as for the risks those species have faced since human arrival. On Caribbean islands, no other columbid has generated more phylogenetic interest and uncertainty than the blue-headed quail-dove,Starnoenas cyanocephala. This endangered Cuban endemic has been considered more similar, both behaviourally and phenotypically, to Australasian species than to the geographically closer ‘quail-dove’ (Geotrygons.l.) species of the Western Hemisphere. Here, we use whole genome sequencing fromStarnoenasand other newly sequenced columbids in combination with sequence data from previous publications to investigate its relationships. Phylogenomic analyses, which represent 35 of the 51 genera currently comprising the Columbidae, reveal that the blue-headed quail-dove is the sole representative of a lineage diverging early in the radiation of columbids.Starnoenasis sister to the species-rich subfamily Columbinae, which is found worldwide. As a highly distinctive evolutionary lineage lacking close modern relatives, we recommend elevating the conservation priority ofStarnoenas. 

Organisms that have repeatedly evolved similar morphologies owing to the same selective pressures provide excellent cases in which to examine specific morphological changes and their relevance to the ecology and evolution of taxa. Hosts of permanent parasites act as an independent evolutionary experiment, as parasites on these hosts are thought to be undergoing similar selective pressures. Parasitic feather lice have repeatedly diversified into convergent ecomorphs in different microhabitats on their avian hosts. We quantified specific morphological characters to determine (i) which traits are associated with each ecomorph, (ii) the quantitative differences between these ecomorphs, and (iii) if there is evidence of displacement among co-occurring lice as might be expected under louse–louse competition on the host. We used nano-computed tomography scan data of 89 specimens, belonging to four repeatedly evolved ecomorphs, to examine their mandibular muscle volume, limb length and three-dimensional head shape data. Here, we find evidence that lice repeatedly evolve similar morphologies as a mechanism to escape host defences, but also diverge into different ecomorphs related to the way they escape these defences. Lice that co-occur with other genera on a host exhibit greater morphological divergence, indicating a potential role of competition in evolutionary divergence. 

Abstract 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. 

Abstract PremiseAmong the slowest steps in the digitization of natural history collections is converting imaged labels into digital text. We present here a working solution to overcome this long‐recognized efficiency bottleneck that leverages synergies between community science efforts and machine learning approaches. MethodsWe present two new semi‐automated services. The first detects and classifies typewritten, handwritten, or mixed labels from herbarium sheets. The second uses a workflow tuned for specimen labels to label text using optical character recognition (OCR). The label finder and classifier was built via humans‐in‐the‐loop processes that utilize the community science Notes from Nature platform to develop training and validation data sets to feed into a machine learning pipeline. ResultsOur results showcase a >93% success rate for finding and classifying main labels. The OCR pipeline optimizes pre‐processing, multiple OCR engines, and post‐processing steps, including an alignment approach borrowed from molecular systematics. This pipeline yields >4‐fold reductions in errors compared to off‐the‐shelf open‐source solutions. The OCR workflow also allows human validation using a custom Notes from Nature tool. DiscussionOur work showcases a usable set of tools for herbarium digitization including a custom‐built web application that is freely accessible. Further work to better integrate these services into existing toolkits can support broad community use.