Rising minimum temperatures contribute to 50 years of occupancy decline among cold-adapted Arctic and boreal butterflies in North America.

Global climate change has been identified as a potential driver of observed insect declines, yet in many regions, there are critical data gaps that make it difficult to assess how communities are responding to climate change. Poleward regions are of particular interest because warming is most rapid while biodiversity data are most sparse. Building on recent advances in occupancy modeling of presence-only data, we reconstructed 50 years (1970-2019) of butterfly occupancy trends in response to rising minimum temperatures in one of the most under-sampled regions of North America. Among 90 modeled species, we found that cold-adapted species are far more often in decline compared with their warm-adapted, more southernly distributed counterparts. Furthermore, in a post hoc analysis using species' traits, we find that species' range-wide average annual temperature is the only consistent predictor of occupancy changes. Species with warmer ranges were most likely to be increasing in occupancy. This trend results in the majority of butterflies increasing in occupancy probability over the last 50 years. Our results provide the first look at macroscale butterfly biodiversity shifts in high-latitude North America. These results highlight the potential of leveraging the wealth of presence-only data, the most abundant source of biodiversity data, for inferring changes in species distributions.

Method matters: pitfalls in analysing phenology from occurrence records.

Large occurrence datasets provide a sizable resource for ecological analyses, but have substantial limitations. Phenological analyses in Fric et al. (2020) were misleading due to inadequate curation and improper statistics. Reanalysing 22 univoltine species with sufficient data for independent analysis, we found substantively different macroscale phenological patterns, including later onset at higher latitude for most species.

Drivers of arthropod biodiversity in an urban ecosystem.

Our world is becoming increasingly urbanized with a growing human population concentrated around cities. The expansion of urban areas has important consequences for biodiversity, yet the abiotic drivers of biodiversity in urban ecosystems have not been well characterized for the most diverse group of animals on the planet, arthropods. Given their great diversity, comparatively small home ranges, and ability to disperse, arthropods make an excellent model for studying which factors can most accurately predict urban biodiversity. We assessed the effects of (i) topography (distance to natural areas and to ocean) (ii) abiotic factors (mean annual temperature and diurnal range), and (iii) anthropogenic drivers (land value and amount of impervious surface) on the occurrence of six arthropod groups represented in Malaise trap collections run by the BioSCAN project across the Greater Los Angeles Area. We found striking heterogeneity in responses to all factors both within and between taxonomic groups. Diurnal temperature range had a consistently negative effect on occupancy but this effect was only significant in Phoridae. Anthropogenic drivers had mixed though mostly insignificant effects, as some groups and species were most diverse in highly urbanized areas, while other groups showed suppressed diversity. Only Phoridae was significantly affected by land value, where most species were more likely to occur in areas with lower land value. Los Angeles can support high regional arthropod diversity, but spatial community composition is highly dependent on the taxonomic group.

Population dynamics and drivers of the eastern monarch (Danaus plexippus) across its full annual cycle: a cross-scale synthesis of a model migratory species.

The monarch butterfly is arguably the best-known butterfly species throughout its global range. Declines in the size of the overwintering colonies in Mexico have sparked controversy regarding the conservation of the species and this controversy has been heightened since the United States Fish and Wildlife Service and International Union for the Conservation of Nature concluded that the eastern monarch populations were threatened (or in the case of United States Fish and Wildlife Service, warranted listing). Drivers of decline vary through space and time. Here, we present a synthesis of longitudinal monarch abundance studies that aim to disentangle the putative drivers of decline from one another. We find remarkable consistency that suggests monarch populations are indeed declining and that potential drivers of such decline shift over time. We strongly encourage future work on the species paired with mechanistic, experimental designs to address some long-standing knowledge gaps.

LepTraits 1.0 A globally comprehensive dataset of butterfly traits.

Here, we present the largest, global dataset of Lepidopteran traits, focusing initially on butterflies (ca. 12,500 species records). These traits are derived from field guides, taxonomic treatments, and other literature resources. We present traits on wing size, phenology,voltinism, diapause/overwintering stage, hostplant associations, and habitat affinities (canopy, edge, moisture, and disturbance). This dataset will facilitate comparative research on butterfly ecology and evolution and our goal is to inspire future research collaboration and the continued development of this dataset.

Current GBIF occurrence data demonstrates both promise and limitations for potential red listing of spiders.

Conservation assessments of hyperdiverse groups of organisms are often challenging and limited by the availability of occurrence data needed to calculate assessment metrics such as extent of occurrence (EOO). Spiders represent one such diverse group and have historically been assessed using primary literature with retrospective georeferencing. Here we demonstrate the differences in estimations of EOO and hypothetical IUCN Red List classifications for two extensive spider datasets comprising 479 species in total. The EOO were estimated and compared using literature-based assessments, Global Biodiversity Information Facility (GBIF)-based assessments and combined data assessments. We found that although few changes to hypothetical IUCN Red List classifications occurred with the addition of GBIF data, some species (3.3%) which could previously not be classified could now be assessed with the addition of GBIF data. In addition, the hypothetical classification changed for others (1.5%). On the other hand, GBIF data alone did not provide enough data for 88.7% of species. These results demonstrate the potential of GBIF data to serve as an additional source of information for conservation assessments, complementing literature data, but not particularly useful on its own as it stands right now for spiders.

Globally distributed occurrences utilised in 200 spider species conservation profiles (Arachnida, Araneae).

BACKGROUND: Data on 200 species of spiders were collected to assess the global threat status of the group worldwide. To supplement existing digital occurrence records from GBIF, a dataset of new occurrence records was compiled for all species using published literature or online sources, from which geographic coordinates were extracted or interpreted from locality description data. NEW INFORMATION: A total of 5,104 occurrence records were obtained, of which 2,378 were from literature or online sources other than GBIF. Of these, 2,308 had coordinate data. Reporting years ranged from 1834 to 2017. Most records were from North America and Europe, with Brazil, China, India and Australia also well represented.

Visualizing natural history collection data provides insight into collection development and bias.

Natural history collections contain estimated billions of records representing a large body of knowledge about the diversity and distribution of life on Earth. Assessments of various forms of bias within the aggregated data associated with specimens in these collections have been conducted across temporal, taxonomic, and spatial domains. Considering that these biases are the sum of biases across all contributing collections to aggregate datasets, the assessment of bias at the collection level is warranted. Interactive visualization provides a powerful tool for the assessment of these biases and insight into the historical development of natural history collections, providing context for where sources of bias may originate and developing historical narratives to clarify our understanding of our own knowledge about life on Earth. Here, I present a case study on using Sankey diagrams to illustrate the development of the entomology type collection at the Academy of Natural Sciences of Drexel University in Philadelphia, Pennsylvania with the hope that extensions of these practices among individual natural history collections are modified and adopted.