The race is not to the swift: long-term data reveal pervasive declines in California's low-elevation butterfly fauna

Understanding the ecology of extinction is one of the primary challenges facing ecologists in the 21st century. Much of our current understanding of extinction, particularly for invertebrates, comes from studies with large geographic coverage but less temporal resolution, such as comparisons between historical collection records and contemporary surveys for geographic regions or political entities. We present a complementary approach involving a data set that is geographically restricted but temporally intensive: we focus on three sites in the Central Valley of California, and utilize 35 years of biweekly (every two weeks) surveys at our most long-sampled site. Previous analyses of these data revealed declines in richness over recent decades. Here, we take a more detailed approach to investigate the mode of decline for this fauna. We ask if all species are in decline, or only a subset. We also investigate traits commonly found to be predictors of extinction risk in other studies, such as body size, diet breadth, habitat association, and geographic range. We find that population declines are ubiquitous: the majority of species at our three focal sites (but not at a nearby site at higher elevation) are characterized by reductions in the fraction of days that they are observed per year. These declines are not readily predicted by ecological traits, with the possible exception of ruderal/non-ruderal status. Ruderal species, in slightly less precipitous decline than non-ruderal taxa, are more dispersive and more likely to be associated with disturbed habitats and exotic hosts. We conclude that population declines and extirpation, particularly in regions severely and recently impacted by anthropogenic alteration, might not be as predictable as has been suggested by other studies on the ecology of extinction.     

Revisiting the evolution of ecological specialization, with emphasis on insect-plant interactions

Ecological specialization is a fundamental and well-studied concept, yet its great reach and complexity limit current understanding in important ways. More than 20 years after the publication of D. J. Futuyma and G. Moreno's oft-cited, major review of the topic, we synthesize new developments in the evolution of ecological specialization. Using insect-plant interactions as a model, we focus on important developments in four critical areas: genetic architecture, behavior, interaction complexity, and macroevolution. We find that theory based on simple genetic trade-offs in host use is being replaced by more subtle and complex pictures of genetic architecture, and multitrophic interactions have risen as a necessary framework for understanding specialization. A wealth of phylogenetic data has made possible a more detailed consideration of the macroevolutionary dimension of specialization, revealing (among other things) bidirectionality in transitions between generalist and specialist lineages. Technological advances, including genomic sequencing and analytical techniques at the community level, raise the possibility that the next decade will see research on specialization spanning multiple levels of biological organization in non-model organisms, from genes to populations to networks of interactions in natural communities. Finally, we offer a set of research questions that we find to be particularly pressing and fruitful for future research on ecological specialization.     

Building phenological models from presence/absence data for a butterfly fauna.

Species phenology is increasingly being used to explore the effects of climate change and other environmental stressors. Long-term monitoring data sets are essential for understanding both patterns manifest by individual species and more complex patterns evident at the community level. This study used records of 78 butterfly species observed on 626 days across 27 years at a site in northern California, USA, to build quadratic logistic regression models of the observation probability of each species for each day of the year. Daily species probabilities were summed to develop a potential aggregate species richness (PASR) model, indicating expected daily species richness. Daily positive and negative contributions to PASR were calculated, which can be used to target optimum sampling time frames. Residuals to PASR indicate a rate of decline of 0.12 species per year over the course of the study. When PASR was calculated for wet and dry years, wet years were found to delay group phenology by up to 17 days and reduce the maximum annual expected species from 32.36 to 30. Three tests to determine how well the PASR model reflected the butterfly fauna dynamics were all positive: We correlated probabilities developed with species presence/absence data to observed abundance by species, tested species' predicted phenological patterns against known biological characteristics, and compared the PASR curve to a spline-fitted curve calculated from the original species richness observations. Modeling individual species' flight windows was possible from presence/absence data, an approach that could be used on other similar records for butterfly communities with seasonal phenologies, and for common species with far fewer dates than used here. It also provided a method to assess sample frequency guidelines for other butterfly monitoring programs.