Global weather and local butterflies: variable responses to a large‐scale climate pattern along an elevational gradient

Abstract

Abstract. Understanding the spatial and temporal scales at which environmental variation affects populations of plants and animals is an important goal for modern population biology, especially in the context of shifting climatic conditions. The El Nin˜o Southern Oscillation (ENSO) generates climatic extremes of interannual variation, and has been shown to have significant effects on the diversity and abundance of a variety of terrestrial taxa. However, studies that have investigated the influence of such large-scale climate phenomena have often been limited in spatial and taxonomic scope. We used 23 years of a long-term butterfly monitoring data set to explore associations between variation in population abundance of 28 butterfly species and variation in ENSO-derived sea surface temperature anomalies (SSTA) across 10 sites that encompass an elevational range of 2750 m in the Sierra Nevada mountain range of California. Our analysis detected a positive, regional effect of increased SSTA on butterfly abundance (wetter and warmer years predict more butterfly observations), yet the influence of SSTA on butterfly abundances varied along the elevational gradient, and also differed greatly among the 28 species. Migratory species revealed the strongest relationships with ENSO-derived SSTA, suggesting that large-scale climate indices are particularly valuable for understanding biotic-abiotic relationships of the most mobile species. In general, however, the ecological effects of large-scale climatic factors are context dependent between sites and species. Our results illustrate the power of long-term data sets for revealing pervasive yet subtle climatic effects, but also caution against expectations derived from exemplar species or single locations in the study of biotic-abiotic interactions.

Increasing neonicotinoid use and the declining butterfly fauna of lowland California

“…The observation that the populations at our focal sites are connected to regional metapopulations is supported by the effectiveness in previous analyses of regional climatic factors (e.g. the El Niñ o Southern Oscillation) when compared with the most local weather (from site-specific weather stations) in predicting butterfly dynamics [10,11].…”

Impacts of a millennium drought on butterfly faunal dynamics

“…Analyses included the following weather variables: maximum and minimum daily temperatures, total precipitation, and a sea surface temperature variable associated with regional conditions [17]. Following previous analyses [15], maximum and minimum temperatures were averaged and precipitation was totaled from the start of September of the previous year through August of the current year.…”

“…First, we used plots of z-standardized data to visualize patterns in phenology (DFF and DLF) and flight days over time; the latter variable, the number of days flying, was expressed as the fraction of days that a species is observed divided by the number of visits to a site per year (this has been referred to as "day positives" in other publications using these data [17]). DFF, DLF, and flight days were z-standardized within species at individual sites and then averaged across species to facilitate comparisons of patterns across sites and years.…”

“…Populations at higher elevations, in contrast, have shown relatively less directional change over time, with the exception of a decline in more dispersive, disturbance-associated species that rely on demographic connections with lower-elevation source populations [15]. Previous analyses of abiotic effects have noted responses to weather that were heterogeneous and idiosyncratic among sites and species [16,17]. For example, associations with climatic variables often differ in sign among congeneric species [18], and even among populations of a single species [17].…”

“…Previous analyses of abiotic effects have noted responses to weather that were heterogeneous and idiosyncratic among sites and species [16,17]. For example, associations with climatic variables often differ in sign among congeneric species [18], and even among populations of a single species [17].…”