DOI: 10.5061/dryad.bg79cnpkr
title: Data from: Pace and shape of life differences drive invasion trajectory in introduced lizards in Hawaii

description: Fast life histories allow colonizing populations to rapidly escape the
 risk of stochastic extinction, while slow life histories can buffer
 against poor conditions. Thus, either strategy can be successful depending
 on the context of the invasion. Recent species-level trait compilations
 have found that introduced reptiles tend to have relatively fast life
 histories. However, life history is highly variable within species and may
 be shaped by the invasion process. Therefore, evaluating population-level
 traits is necessary for understanding the trajectory of specific
 invasions. We measured individual somatic growth and population growth
 under controlled field conditions in three species of introduced lizards
 in Hawaii, which share similar species-level traits, to determine how life
 history may be affecting community dynamics in this ongoing invasion. We
 found a trade-off along the fast-slow life history axis: Anolis sagrei
 grew the fastest and had the lowest survival, Phelsuma laticauda grew the
 slowest and had the highest survival, and Anolis carolinensis was
 intermediate. By the end of the year, both the fastest and slowest species
 had achieved densities that were approximately twice as high as those of
 the intermediate species. We also found differences along the shape of the
 life axis: Anolis carolinensis only reproduced during half the year, while
 the other species reproduced year-round. These results suggest that
 successful establishment by A. carolinensis would be most impacted by when
 during the year introductions occur, and that they would be at the highest
 risk of stochastic extinction. Despite having similar life history traits
 at the species level, these species have very different life histories in
 Hawaii, and both the shape of life and pace of life traits are likely
 important for determining establishment and spread. Our results support
 increased attention on the shape of life traits and on intraspecific trait
 variation in order to better understand the establishment and spread of
 introduced species.
description: # Data from: Pace and shape of life differences drive invasion trajectory
 in introduced lizards in Hawaii
 [https://doi.org/10.5061/dryad.bg79cnpkr](https://doi.org/10.5061/dryad.bg79cnpkr) ## Description of the data and file structure Contains data and R files to reproduce the model from "Pace and shape of life differences drive invasion trajectory in introduced lizards in Hawaii." The SVL is measured in mm, and the interval is measured in years. ### Files and variables **Data files**: data files are denoted with X, which can take on the values ASAG, ACAR, or PLAT, denoting each of *Anolis sagrei*, *Anolis carolinensis*, or *Phelsuma laticauda*. X_growth1.csv and X_growth2.csv: data for modeling growth using snout-vent length measurements * SVL1_1: Lagged snout-vent-length data used as predictor variable - SVL2_1: Snout_-vent-length data to be modeled * growSex_1: Sex of the current animal - growInd_1: Numeric variable used to uniquely identify an individual * m_1: Time interval between observations in SVL2_1 and SVL1_1 X_timePredictors1.csv and X_timePredictors2.csv: predictors that vary in time. * IncludeInds: index of individuals to include in the population estimate at each time step. - Type: type of sampling occasion recapture (coded as 0) or resight (coded as 1). * intervals: time interval between sampling occasions. - remaining rows contain measured lengths on each capture occasion X_recapresight1.csv and X_recapresight2.csv: contain predictors that vary by individual and recapture and resight observations for each visit. * Sex: The sex of this individual, either female (coded as 0) or male (coded as 1) - f1: visit the date this individual was first observed on. * The remaining column is associated with whether the individual was detected (coded as 1) or not (coded as 0) on each visit. An NA denotes that an individual had not been detected at all by this visit. ## Code/software RunModels.R: Running this file in R will read in all datasets and format them as needed to run the joint CJS-growth model. CJS_growth.jags: This JAGS file codes the model. It is called by RunModels.R.