Species Traits as Filters of Climate-Induced Range Expansion DIANE DEBINSKI JEREMY KERR AND MAXIM LARRIVÉE IOWA STATE UNIVERSITY, UNIVERSITY OF OTTAWA, INSECTARIUM DE MONTRÉAL

INTRODUCTION As climate warms, species are expected to move poleward or up in elevation to track the environmental conditions to which they are most adapted (Parmesan et al. 1999; Chen, Hill et al. 2011) However, this movement and the subsequent colonization of new habitat may be affected by species-specific habitat affinities and life history traits that facilitate or impede range expansion map from vidiani.com 0°C0°C Warming over time Diagram courtesy of Jay Fitzsimmons

INTRODUCTION Even if the “thermal envelope” is ideal, the vegetation conditions or other environmental parameters may not be conducive Similarly, traits such as wingspan or voltinism may act as a filter in defining which species can successfully colonize new regions Build on use of trait analysis: Ockinger et al. (2010) Eco. Letters Debinski et al. (2013) Ecology

INTRODUCTION (CONT.) Using massive, long-term butterfly datasets, we asked: 1. How do abundance patterns change over time from early vs. recent time? 2. How do butterfly traits affect the changes in abundance?

Red to Blue = High to low butterfly species richness Red to Blue = Increasing to decreasing butterfly species richness in the past century

HYPOTHESES  Species with larger wingspans and more generations per year would respond more quickly to climate change and thus show positive trends in abundance.  Generalist species would show increasing trends, whereas specialists would be more likely to show decreasing trends.

 Compared species abundance patterns from historical time periods ( ) and recent time periods ( ).  Restricted analysis to ~100 species that were well sampled (i.e., they had abundances of 50 or more during each time period) BUTTERFLY METHODS: BUTTERFLY DATA

 Wingspan  Preferred habitat - open (e.g., grassland), closed (e.g., forest) or edge (e.g., riparian edge).  Moisture - wet habitat, dry habitat or both.  Voltinism generations per year. BUTTERFLY METHODS: TRAITS Sources: Scott, Opler, BAMONA, Iftner, Acorn, Layberry, xerces.org., natureserve.org

Grid cells at a 400 km resolution (equal area projection) BUTTERFLY METHODS

A standardized abundance was calculated for each species by subtracting the mean count over all species and locations within each time frame. For each species at each location standardized abundance at time t 1 was subtracted from the standardized abundance at time t 2 to calculate a standardized difference in abundance. We then used a linear model to model standardized difference in abundance: std_diff ~ y + wingspan + habitat_type + moisture + voltinism + wingspan * y + habitat_type * y + moisture * y + voltinism * y Where y= latitude BUTTERFLY METHODS: STANDARDIZING ABUNDANCE

RESULTS: TRAITS AND ABUNDANCE CHANGES Table 2: Coefficients from the full model with all main effects for difference in standardized abundance. All factors were significant. Model wingspan 1.47 Moisture: dry (base= wet) moisture: both (base=wet) Voltinism2 (base=1) Voltinism3 (base=1) Note: interaction with latitude was also significant for all factors

PIERIS RAPAE, PHYCIODES PUCHELLA, PAPILIO ZELICAON, AND CUPIDO AMYNTULA. HABITAT GENERALISTS

3 GENERATIONS PER YEAR Pieris rapae, Vanessa cardui, and Vanessa virginiensis

RESULTS: PROPORTIONAL CHANGE IN ABUNDANCE between (t 1 ) and (t 2 ) for species with 1 (left), 2 (middle), or 3 (right) generations per year. 1 Generation 2 Generations 3 Generations

SUMMARY: ABUNDANCE PATTERNS Species with the following traits showed increases in relative abundance over time: o Larger wingspans o More generalized habitat requirements o Higher growth rates (voltinism) No significant differences relative to habitat affinities

SYNTHESIS Our results support the hypothesis that traits are indeed acting as an important filter differentiating which species will respond most quickly to climate change.

ACKNOWLEDGEMENTS  University of Ottawa, Distinguished Visiting Researcher Program  Iowa State University, Faculty Professional Development Assignment  E-butterfly:  Statistical consulting: Lendie Follett, ISU