Historical Mark Release Recapture (MRR) studies of selection and plasticity in butterflies

Historical data on plasticity, selection and evolution of phenotypic traits in the wild provide an important resource for understanding how populations are responding to ongoing anthropogenic environmental changes. We provide the datasets associated with a series of Mark Release Recapture (MRR) studies that quantified seasonal variation in plasticity and phenotypic selection in a natural population of Pontia occidentalis butterflies in southcentral WA USA between 1989–1996. The studies examined both natural and experimentally-generated variation in two types of traits: wing melanin pattern on each wing surface; and wing and body size and shape. Papers published during the 1990s that summarized the results of these studies demonstrated phenotypic plasticity and directional selection on some of the traits that varied with seasonal weather conditions. Here we present the original, underlying data for each of the nine studies.  These data may be valuable for exploring newer statistical approaches for modeling MRR studies of selection and plasticity, and for evaluating historical changes in selection and evolution of traits related to climate adaptation in response to ongoing climate and other environmental changes.

Methods
These datasets provide the original data for a series of mark-recapture studies, and associated research publications, with Pontia occidentalis butterflies near Corfu WA between 1989 and 1996.  Details of the study site, methodology, and associated publications for each study are described in the Combined ReadMe file and in the associated document file: Kingsolver Historical MRR datasets for Pontia occidentalis.doc, and in the list of related works below. The dataset for each study is provided as a separate MS .xls file.

Most datasets consisted of two original files: one that contained initial capture data (treatment group, wing condition, mass, etc.), and one that contained subsequent recapture data (behavior when recaptured, time of day, etc.). For each experiment, we followed the same workflow:

We scanned and transcribed the field notebooks as written into the two separate files
We checked for errors by checking for duplicate IDs, mistyped treatment group labels, etc.

We concatenated these two datafiles into one long datafile by marking the initial capture as recapture # 0. By doing so, we could keep all of the affiliated data unique to each sample and be able to analyze the recaptures alongside these data.

Due to the shared workflow, all of the final datafiles have the same structure: a set of initial captures as the beginning rows of the datafile (marked as recapture #0), and then subsequent recaptures (with duplicated identifying data) following in later rows. Details are provided in the Combined ReadMe file.