Bumble Bee Watch community science program increases scientific understanding of an important pollinator group across Canada and the USA

In a time of increasing threats to bumble bees (Hymenoptera: Apidae: Bombus), it is important to understand their ecology and distribution. As experts are limited in resources to conduct field surveys, there is potential for community scientists to help. The Bumble Bee Watch (BBW) community science program involves volunteers taking photos of bumble bees in Canada and the USA and submitting them, along with geographic and optional plant information, to a website or through an app. Taxon experts then verify the bee species identification. The Bumble Bees of North America database (BBNA) stores data (no photographs) collected and identified by more traditional scientific methods over the same range. Here we compared BBW data to BBNA data over all years and just 2010-2020 to understand the scientific contribution of community scientists to the state of the knowledge about native bumble bees. We found that BBW had similar geographic and species coverage as BBNA. It had records from all 63 provinces, states, and territories where bumble bees occur (including four more than BBNA in 2010-2020), and represented 41 of the 48 species in BBNA (with ten more species than BBNA in 2010-2020). While BBW contributed only 8.50% of records overall, it contributed 25.06% of all records over 2010-2020. BBW confirmed the persistence of species and identified new locations of species, both inside and outside of the previously known extent of occurrences. BBW also contributed a wealth of ecological information, such as unique plant genera and species data for almost all the bee species. Thus, while BBW had fewer bee records than the BBNA database overall, it helped to fill in data gaps and provided novel information, complementing the traditional methods. This community science program is valuable in helping to inform conservation management for bumble bee species.

Increasing biodiversity knowledge through social media: A case study from tropical Bangladesh

Citizen science programs are becoming increasingly popular among naturalists but remain heavily biased taxonomically and geographically. However, with the explosive popularity of social media and the near-ubiquitous availability of smartphones, many post wildlife photographs on social media. Here, we illustrate the potential of harvesting these data to enhance our biodiversity understanding using Bangladesh, a tropical biodiverse country, as a case study. We compared biodiversity records extracted from Facebook with those from the Global Biodiversity Information Facility (GBIF), collating geospatial records for 1013 unique species, including 970 species from Facebook and 712 species from GBIF. Although most observation records were biased toward major cities, the Facebook records were more evenly spatially distributed. About 86% of the Threatened species records were from Facebook, whereas the GBIF records were almost entirely Of Least Concern species. To reduce the global biodiversity data shortfall, a key research priority now is the development of mechanisms for extracting and interpreting social media biodiversity data.

Estimating the movements of terrestrial animal populations using broad-scale occurrence data

As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.

Mapping the premigration distribution of eastern Monarch butterflies using community science data

Knowing the distribution of migratory species at different stages of their life cycle is necessary for their effective conservation. For the Monarch butterfly (Danaus plexippus), although its overwintering distribution is well known, the available information on premigration distribution is limited to the studies estimating the natal origins of overwintering Monarchs in Mexico (i.e., postmigration data). However, the premigration distribution and the natal origins of overwintering Monarchs can be equivalent only if we assume that migrating Monarchs have the same mortality rate irrespective of their origins. To estimate Monarchs' premigration distribution, we used data reported by community scientists before Monarchs start their fall migration, that is, before migration mortality, and controlled for sampling bias. Our premigration distribution map indicated that Minnesota, Texas, and Ontario are the states/provinces with the highest abundance of Monarch in North America. Although this higher estimated abundance can be related to the large sizes of these states/provinces, this information is still important because it identifies the management jurisdictions with the largest responsibility for the conservation of the premigration population of Monarchs. Our premigration distribution map will be useful in future studies estimating the rates, distribution, and causes of mortality in migrating Monarchs.

Low prevalence of the parasite Ophryocystis elektroscirrha at the range edge of the eastern North American monarch (Danaus plexippus) butterfly population

Every year monarch butterflies (Danaus plexippus (Linnaeus, 1758)) from the eastern North American population migrate from Mexico to southern Canada in the spring. This northward migration has been shown to reduce monarch infection with the host-specific parasite Ophryocystis elektroscirrha McLaughlin and Myers, 1970 (OE); yet, the prevalence of OE at their range limits and the mechanism(s) responsible are unknown. We assessed OE infection levels of monarchs at the northern edge of the eastern population distribution around Ottawa, Ontario, Canada, and found extremely low levels of infection (∼1% with upper confidence intervals close to 3%). Low OE infection levels are likely due to low densities of monarchs in this region and (or) migratory escape effects, where migrating individuals leave behind areas with high density of conspecifics and high potential for parasite accumulation and transmission. Future work should aim to disentangle the relative contribution of these two mechanisms for governing the decrease in parasitism at the range limits of migratory populations.