Word count: 4369Data and Code Availability: Data and R scripts for all analyses are archived on Zenodo ( ABSTRACT AimsSpecies distributions result from both biotic and abiotic interactions across large spatial scales.The interplay of these interactions as climate changes quickly has been understudied, particularly in herbivorous insects. Here, we investigate the relative impacts these influences on the putative northern range expansion of the giant swallowtail butterfly in North America. LocationNorth America. Time period 1959-2018. Major taxa studiedEastern Giant swallowtail, Papilio cresphontes (Lepidoptera: Papilionidae); common hop tree, Ptelea trifoliata ; common prickly ash, Zanthoxylum americanum ; southern prickly ash, Zanthoxylum clava-herculis (Saphidales: Rutaceae). MethodsWe used data from museum collections and citizen science repositories to generate species distribution models. Distribution models were built for each species over two time periods (T1 = 1959-1999; T2 = 2000-2018). Results Models for P. cresphontes and associated host plants had high predictive accuracy onspatially-explicit test data (AUC 0.810-0.996). Occurrence data align with model outputs, providing strong evidence for a northward range expansion in the last 19 years (T2) by P.cresphontes . Host plants have shifted in more complex ways, and result in a change in suitable habitat for P. cresphontes in its historic range. P. cresphontes has a northern range which now closely aligns with its most northern host plant -continued expansion northward is unlikely, and historic northern range limits were likely determined by abiotic, not biotic, factors. Main conclusionsBiotic and abiotic factors have driven the rapid northern range expansion in the giant swallowtail butterfly across North America in the last 20 years. A number of bioclimatic variables are correlated with this expansion, notably an increase in mean annual temperature and minimum winter temperature. We predict a slowing of northward range expansion in the next 20-50 years as butterflies are now limited by the range of host plants, rather than abiotic factors.
The growth of biodiversity data sets generated by citizen scientists continues to accelerate. The availability of such data has greatly expanded the scale of questions researchers can address. Yet, error, bias, and noise continue to be serious concerns for analysts, particularly when data being contributed to these giant online data sets are difficult to verify. Counts of birds contributed to eBird, the world’s largest biodiversity online database, present a potentially useful resource for tracking trends over time and space in species’ abundances. We quantified counting errors in a sample of 1406 eBird checklists by comparing numbers contributed by birders (N=246) who visited a popular birding location in Oregon, USA, with numbers generated by a professional ornithologist engaged in a long-term study creating benchmark (reference) measurements of daily waterbird counts. We focused on waterbirds, which are easily visible at this site. We evaluated potential predictors of count differences, including characteristics of contributed checklists, of each species, and of time of day and year. Count differences were biased toward undercounts, with more than 75% of counts being below the daily benchmark value. When only checklists that actually reported a species known to be present were included, median count errors were −29.1% (range: 0 to −42.8 %; N=20 species). Model sets revealed an important influence of each species’ reference count, which varied seasonally as waterbird numbers fluctuated, and of percent of species known to be present each day that were included on each checklist. That is, checklists indicating a more thorough survey of the species richness at the site also had, on average, lower counting errors. However, even on checklists with the most thorough species lists, counts were biased low and exceptionally variable in their accuracy. To improve utility of such bird count data, we suggest three strategies to pursue in the future. One is to assess additional options for analytically determining how to select checklists that have the highest probability of including less biased count data, as well as exploring options for correcting bias during the analysis stage. Another is to add options for users to provide additional information that helps analysts choose checklists, such as an option for users to tag checklists where they focused on obtaining accurate counts. We also recommend exploration of opportunities to effectively calibrate citizen-science bird count data by establishing a formalized network of marquis sites where dedicated observers regularly contribute carefully collected benchmark data.
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