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This Level 2 data package contains species level estimated abundances, including zero counts, and estimated mean number of female mosquitoes per trap derived from the NEON Mosquitoes sampled from CO2 traps (DP1.10043.001), RELEASE-2024 Level 0 data (https://doi.org/10.48443/3cyq-6v47). The data set includes mosquito records of traps collecting mosquito samples at night, for up to 24 trap hours, across a total of 20 terrestrial core and 27 terrestrial gradient sites from 2014 to 2022. To ensure high confidence in abundance estimates, records were only included when at least 90% of collected individuals were identified to sex, and 90% of female specimens were identified to species. Information across multiple QC/QA fields within the NEON mosquito data was evaluated to identify and exclude records where confidence in estimated abundances may have been compromised. Species level zero counts were added for all species collected at least once within the sampling year and trap location. Additionally, species level zero counts were included for trap events where only male mosquitoes had been collected or where QC/QA remarks indicated traps were inactive due to cold temperatures. The data set provides an analysis ready time series of estimated abundances across NEON sites and plots. An R Markdown file that contains descriptions of the QC/QA and data filtering steps along with annotated code, as well as data tables used to filter active and inactive trap events based on QC/QA fields, are published with the data package. Any questions about this data package should be directed to Amely Bauer listed under contacts. 

Abstract Insects often exhibit irruptive population dynamics determined by environmental conditions. We examine if populations of theCulex tarsalismosquito, a West Nile virus (WNV) vector, fluctuate synchronously over broad spatial extents and multiple timescales and whether climate drives synchrony inCx. tarsalis, especially at annual timescales, due to the synchronous influence of temperature, precipitation, and/or humidity. We leveraged mosquito collections across 9 National Ecological Observatory Network (NEON) sites distributed in the interior West and Great Plains region USA over a 45-month period, and associated gridMET climate data. We utilized wavelet phasor mean fields and wavelet linear models to quantify spatial synchrony for mosquitoes and climate and to calculate the importance of climate in explainingCx. tarsalissynchrony. We also tested whether the strength of spatial synchrony may vary directionally across years. We found significant annual synchrony inCx. tarsalis, and short-term synchrony during a single period in 2018. Mean minimum temperature was a significant predictor of annualCx. tarsalisspatial synchrony, and we found a marginally significant decrease in annualCx. tarsalissynchrony. SignificantCx. tarsalissynchrony during 2018 coincided with an anomalous increase in precipitation. This work provides a valuable step toward understanding broadscale synchrony in a WNV vector. 

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. 

Abstract The “small-eared” species group of Urocitellus ground squirrels (Sciuridae: Xerinae: Marmotini) is endemic to the Great Basin, United States, and surrounding cold desert ecosystems. Most specific and subspecific lineages in this group occupy narrow geographic ranges, and some are of significant conservation concern; despite this, current taxonomy remains largely based on karyotypic or subtle pelage and morphological characteristics. Here, we leverage 2 multilocus DNA sequence data sets and apply formal species delimitation tests alongside morphometric comparisons to demonstrate that the most widespread small-eared species (U. mollis Kennicott, 1863 sensu lato; Piute Ground Squirrel) is comprised of 2 nonsister and deeply divergent lineages. The 2 lineages are geographically separated by the east-west flowing Snake River in southern Idaho, with no sites of sympatry currently known. Based on robust support across the nuclear genome, we elevate populations previously attributed to U. mollis from north of the Snake River to species status under the name Urocitellus idahoensis (Merriam 1913) and propose the common name “Snake River Plains Ground Squirrel” for this taxon. We delimit 2 subspecies within U. idahoensis; U. i. idahoensis (Merriam 1913) in western Idaho and U. i. artemesiae (Merriam 1913) in eastern Idaho. Urocitellus idahoensis is endemic to Idaho and has a maximal range area of roughly 29,700 km2 spanning 22 counties but occurs discontinuously across this area. Our work substantially expands knowledge of ground squirrel diversity in the northern Great Basin and Columbia Plateau and highlights the difficulty in delimiting aridland mammals whose morphological attributes are highly conserved. 

Biological invasions are profoundly altering Earth’s ecosystems, but generalities about the effects of nonnative species on the diversity and productivity of native communities have been elusive. This lack of generality may reflect the limited spatial and temporal extents of most previous studies. Using >5 million tree measurements across eastern US forests from 1995 to 2023, we quantified temporal trends in tree diversity and biomass. We then analyzed community-level changes in native tree diversity and biomass in relation to nonnative tree invasion and native species colonization. Across the entire eastern United States, native tree species richness decreased over time in plots where nonnatives occurred, whereas nonnative species richness and the biomass of both natives and nonnatives increased over time. At the community scale, native richness tended to decline following nonnative invasion, whereas native biomass and richness-independent measures of trait and phylogenetic diversity tended to remain stable. These patterns can be explained by the rarity of the displaced native species and their functional and phylogenetic similarity to native species that survived nonnative invasions. In contrast, native survivors tended to be functionally distinct from nonnative invaders, suggesting an important role for niche partitioning in community dynamics. Colonization by previously absent native species was associated with an increase in native richness (beyond the addition of native colonizers), which contrasts with declines in native richness that tended to follow nonnative invasion. These results suggest a causal role for nonnative species in the native richness decline of invaded communities. 

Abstract Nitrogen (N)-fixing symbiosis is critical to terrestrial ecosystems, yet possession of this trait is known for few plant species. Broader presence of the symbiosis is often indirectly determined by phylogenetic relatedness to taxa investigated via manipulative experiments. This data gap may ultimately underestimate phylogenetic, spatial, and temporal variation in N-fixing symbiosis. Still needed are simpler field or collections-based approaches for inferring symbiotic status. N-fixing plants differ from non-N-fixing plants in elemental and isotopic composition, but previous investigations have not tested predictive accuracy using such proxies. Here we develop a regional field study and demonstrate a simple classification model for fixer status using nitrogen and carbon content measurements, and stable isotope ratios (δ¹⁵N and δ¹³C), from field-collected leaves. We used mixed models and classification approaches to demonstrate that N-fixing phenotypes can be used to predict symbiotic status; the best model required all predictors and was 80–94% accurate. Predictions were robust to environmental context variation, but we identified significant variation due to native vs. non-native (exotic) status and phylogenetic affinity. Surprisingly, N content—not δ¹⁵N—was the strongest predictor, suggesting that future efforts combine elemental and isotopic information. These results are valuable for understudied taxa and ecosystems, potentially allowing higher-throughput field-based N-fixer assessments. 

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. 

Abstract PremisePlant trait data are essential for quantifying biodiversity and function across Earth, but these data are challenging to acquire for large studies. Diverse strategies are needed, including the liberation of heritage data locked within specialist literature such as floras and taxonomic monographs. Here we report FloraTraiter, a novel approach using rule‐based natural language processing (NLP) to parse computable trait data from biodiversity literature. MethodsFloraTraiter was implemented through collaborative work between programmers and botanical experts and customized for both online floras and scanned literature. We report a strategy spanning optical character recognition, recognition of taxa, iterative building of traits, and establishing linkages among all of these, as well as curational tools and code for turning these results into standard morphological matrices. ResultsOver 95% of treatment content was successfully parsed for traits with <1% error. Data for more than 700 taxa are reported, including a demonstration of common downstream uses. ConclusionsWe identify strategies, applications, tips, and challenges that we hope will facilitate future similar efforts to produce large open‐source trait data sets for broad community reuse. Largely automated tools like FloraTraiter will be an important addition to the toolkit for assembling trait data at scale. 

Recent reports of insect declines have raised concerns about the potential for concomitant losses to ecosystem processes. However, understanding the causes and consequences of insect declines is challenging, especially given the data deficiencies for most species. Needed are approaches that can help quantify the magnitude and potential causes of declines at levels above species. Here we present an analytical framework for assessing broad‐scale plant–insect phenologies and their relationship to community‐level insect abundance patterns. We intentionally apply a species‐neutral approach to analyse trends in phenology and abundance at the macroecological scale. Because both phenology and abundance are critical to ecosystem processes, we estimate aggregate metrics using the overwintering (diapause) stage, a key species trait regulating phenology and environmental sensitivities. This approach can be used across broad spatiotemporal scales and multiple taxa, including less well‐studied groups. Using community (‘citizen’) science butterfly observations from multiple platforms across the Eastern USA, we show that the relationships between environmental drivers, phenology and abundance depend on the diapause stage. In particular, egg‐diapausing butterflies show marked changes in adult‐onset phenology in relation to plant phenology and are rapidly declining in abundance over a 20‐year span across the study region. Our results also demonstrate the negative consequences of warmer winters for the abundance of egg‐diapausing butterflies, irrespective of plant phenology. In sum, the diapause stage strongly shapes both phenological sensitivities and developmental requirements across seasons, providing a basis for predicting the impacts of environmental change across trophic levels. Utilizing a framework that ties thermal performance across life stages in relation to climate and lower‐trophic‐level phenology provides a critical step towards predicting changes in ecosystem processes provided by butterflies and other herbivorous insects into the future.