1. Intraspecific variation plays important roles in ecology and evolution. Yet, information on how species and populations vary remains scarce, particularly for insects and regarding functional traits. This lack of knowledge can be problematic in trait-based ecology because traditional approaches assume negligible intraspecific variation, even for analyses that assess highly variable taxa.2. We measured 291 Arctic fritillary butterflies (Boloria chariclea) to assess the intraspecific variation in one population of this species, evaluating (i) how wingspan of Arctic fritillaries varies in relation to the other species of its community, and (ii) how well wingspan, a measure of body size, predicts weight, a measure of body mass.3. Wingspan of Arctic fritillaries varied between 2.62 and 4.07 cm, with the 95% interval range, including ∼33% (14/42) of the species in the community, and ∼30% (84/279) of the butterflies of Canada. The relationship between wingspan and weight was significant (β wingspan = 0.002, SE = 0.0008, P < 0.001), but relatively weak (R 2 adj = 0.31, F 2,288 = 67.82, P < 0.001).4. We discuss our findings in relation to the assumption of species homogeneity and the use of proxies in the analysis of species traits, complementing our case study with simulations to illustrate how intraspecific and interspecific variation interact in determining when traditional trait analyses are robust. We suggest entomologists interested in trait analyses should critically evaluate how intraspecific variation could affect their inference, especially when evaluating species that are highly sexually dimorphic, phenotypically plastic, and/or distributed across broad environmental and spatial clines.
Estimating distribution and abundance of species depends on the probability at which individuals are detected. Butterflies are of conservation interest worldwide, but data collected with Pollard walks—the standard for national monitoring schemes—are often analyzed assuming that changes in detectability are negligible within recommended sampling criteria. The implications of this practice remain poorly understood. Here, we evaluated the effects of sampling conditions on butterfly counts from Pollard walks using the Arctic fritillary, a common but cryptic butterfly in boreal forests of Alberta, Canada. We used an open population binomial N‐mixture model to disentangle the effects of habitat suitability and phenology on abundance of Arctic fritillaries, and its detectability by sampling different conditions of temperature, wind, cloud cover, and hour of the day. Detectability varied by one order of magnitude within the criteria recommended for Pollard walks (P varying between 0.04 and 0.45), and simulations show how sampling in suboptimal conditions increases substantially the risk of false‐absence records (e.g., false‐absences are twice as likely than true‐presences when sampling 10 Arctic fritillaries at P = 0.04). Our results suggest that the risk of false‐absences is highest for species that are poorly detectable, low in abundance, and with short flight periods. Analysis with open population binomial N‐mixture models could improve estimates of abundance and distribution for rare species of conservation interest, while providing a powerful method for assessing butterfly phenology, abundance, and behavior using counts from Pollard walks, but require more intensive sampling than conventional monitoring schemes.
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