# Propagule size and sex ratio influence colonisation dynamics after introduction of a non-native lizard

Amélie Fargevieille, Aaron Reedy, Ariel Kahrl, Timothy Mitchell, Andrew Durso, David Delaney, Phillip Pearson, Robert Cox, Daniel Warner

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Amélie Fargevieille, Aaron Reedy, Ariel Kahrl, Timothy Mitchell, Andrew Durso, et al. Propagule size and sex ratio influence colonisation dynamics after introduction of a non-native lizard. Journal of Animal Ecology, 2022, 91 (4), pp.845-857. 10.1111/1365-2656.13671. hal-04950945

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## Propagule size and sex ratio influence colonisation dynamics after introduction of a non-native lizard

<table><tr><td>Journal:</td><td>Journal of Animal Ecology</td></tr><tr><td>Manuscript ID</td><td>Draft</td></tr><tr><td>Manuscript Type:</td><td>Research Article</td></tr><tr><td>Date Submitted by the Author:</td><td>n/a</td></tr><tr><td>Complete List of Authors:</td><td>Fargevieille, Amelie; Auburn University Department of Biological Sciences, Biological Sciences<br>Reedy, Aaron; Auburn University, Biological Sciences<br>Kahrl, Ariel; Stockholm University, Zoology/Ethology<br>Mitchell, Timothy; University of Minnesota System, Ecology, Evolution and Behavior<br>Durso, Andrew; Florida Gulf Coast University, Department of Biology<br>Delaney, David; Iowa State University<br>Pearson, Phillip; University of Canberra, Centre for Conservation, Ecology and Genomics<br>Cox, Robert; University of Virginia, Biology<br>Warner, Daniel; Auburn University, Biological Sciences</td></tr><tr><td>Key-words:</td><td>colonisation, propagule size, sex ratio, Anolis sagrei, population estimation, population growth</td></tr></table>
<figure><img src="image_2.png" /></figure>

After manipulating propagule composition on nine experimental islands, we found a) a positive correlation between propagule size and offspring production in the two years following introduction while b) populations with female-biased propagules had positive growth and those with male-biased propagules had negative growth during the first year of introduction.

392x103mm (96 x 96 DPI)
# Propagule size and sex ratio influence colonisation dynamics after introduction of a non-native lizard

Amélie Fargevieille<fnref n="1" />, Aaron M. Reedy<fnref n="1" />,<fnref n="2" />, Ariel F. Kahrl<fnref n="2" />,<fnref n="3" />, Timothy S. Mitchell<fnref n="1" />,<fnref n="4" />,<fnref n="5" />, Andrew M. Durso<fnref n="6" />, David M. Delaney<fnref n="4" />,<fnref n="7" />, Phillip R. Pearson<fnref n="1" />,<fnref n="7" />,<fnref n="8" />, Robert M. Cox<fnref n="2" />, Daniel A. Warner<fnref n="1" />,<fnref n="4" />,<fnref n="7" />

**Corresponding author:** Amélie Fargevieille, 101 Rouse Life Sciences, Auburn AL-36830, USA, afargevieille@gmail.com

<footnote n="1">¹Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA</footnote>

<footnote n="2">²Department of Biology, University of Virginia, Charlottesville, VA 22904, USA</footnote>

<footnote n="3">³Department of Zoology/Ethology, Stockholm University, Svante Arrhenius väg 18B SE-10691, Stockholm, Sweden</footnote>

<footnote n="4">⁴Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011, USA</footnote>

<footnote n="5">⁵Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota 55108, USA</footnote>

<footnote n="6">⁶Department of Biological Sciences, Florida Gulf Coast University, Fort Myers, FL 33965, USA</footnote>

<footnote n="7">⁷Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA</footnote>

<footnote n="8">⁸Centre for Conservation, Ecology and Genomics, University of Canberra, Bruce, ACT 2617, Australia</footnote>
# 1 Abstract

1. The composition of founding populations plays an important role in colonisation dynamics and can influence population growth during early stages of biological invasion. Specifically, founding populations with small propagules (i.e., low number of founders) are vulnerable to the Allee effect and have reduced likelihood of establishment compared to those with large propagules. The founding sex ratio can also impact establishment via its influence on mating success and offspring production.

2. Our goal was to test the effects of propagule size and sex ratio on offspring production and annual population growth following introductions of a non-native lizard species (*Anolis sagrei*). We manipulated propagule composition on nine small islands, then examined offspring production, population growth and survival rate of founders and their descendants encompassing three generations.

3. By the third reproductive season, per capita offspring production was higher on islands seeded with a relatively large number of individuals, but population growth was not associated with propagule size. Propagule sex ratio did not affect offspring production in the first year, but populations with a female-biased propagule had positive growth and those with a male-biased propagule had negative growth. Populations were not affected by propagule sex ratio in subsequent years, possibly due to rapid shifts towards balanced (or slightly female-biased) population sex ratios.

4. Overall, we show that different components of population fitness have different responses to propagule size and sex ratio in ways that could affect early stages of biological invasion. Despite these effects, the short lifespan and high fecundity of *A. sagrei* likely helped small populations to overcome Allee effects and enabled all populations to successfully establish.
24 5. Our rare experimental manipulation of propagule size and sex ratio can inform predictions of colonisation dynamics in response to different compositions of founding populations, which is critical in the context of reintroduction programs and invasion dynamics.

27
28 **Keywords:** colonisation – propagule size – sex ratio – *Anolis sagrei* – population estimation – population growth

30
31 **Introduction**

32 Biological invasions threaten biodiversity, alter ecosystems, and have major economic 33 costs (Lockwood et al., 2013). Introductions of non-native species occur frequently, and why some 34 introductions lead to rapid population growth whereas others grow slowly or fail to establish can 35 depend on numerous factors. Indeed, establishment success is often positively related to the 36 number of founders or introduction events, and can be influenced by several other factors related 37 to population demographics (e.g., sex ratio, mortality and birth rates), genetic and environmental 38 factors (e.g., inbreeding, genetic drift, environmental stochasticity), and organismal biology (e.g., 39 physiological tolerances, life-history characteristics) (Allendorf & Lundquist, 2003; Lockwood et 40 al., 2013; Simberloff, 2009; Tabak et al., 2018). Additionally, barriers during different stages of 41 the invasion process (introduction, establishment, and spread) may affect the dynamics of newly 42 introduced organisms (Blackburn et al., 2011). For example, newly introduced populations are 43 typically characterised by an unstable stage structure, which may be a barrier to establishment, 44 particularly in small populations. This unstable stage structure can lead to highly fluctuating 45 population sizes over a short term (referred to as transient dynamics) before achieving a more
stable structure over a longer term (Iles et al., 2016; Stott et al., 2011); this demographic
stochasticity can increase the risk of establishment failure.

Many studies show that propagule pressure, which consists of the number of introduced
individuals (propagule size) and the number of introduction events (propagule number), is an
important determinant of establishment success of invasive species (Britton & Gozlan, 2013;
Simberloff, 2009). These two components of propagule pressure are often positively associated
with establishment success and population growth (Lockwood et al., 2013). Small propagules are
vulnerable to the Allee effect, defined as a positive relationship between components of individual
fitness and either numbers or density of conspecifics (Stephens et al., 1999). Under the Allee effect,
small populations may have a low or even negative growth rate and an increased probability of
extinction, due to genetic inbreeding, demographic stochasticity, or reduction of social interactions
(Stephens & Sutherland, 1999). The Allee effect has been documented in many species and often
varies with life-history characteristics (Courchamp et al., 1999). Importantly, the Allee effect can
decrease invasion success for propagules of small size. For example, populations of the invasive
moth *Lymantria dispar* have a high probability of extinction when the number of individuals
detected (an index of population size) is below a critical threshold (Liebhold & Bascompte, 2003;
Tobin et al., 2009). A large propagule number may overcome the negative effects of small
propagule sizes (Britton & Gozlan, 2013; Simberloff, 2009) as more immigrants over time may
reduce the risk of some individuals encountering unfavourable conditions and/or favour genetic
admixture (Rius & Darling, 2014). However, repeated introductions may also result in outbreeding
depression or may be so infrequent that introduced populations go extinct before a subsequent
introduction occurs (Lockwood et al., 2013).
The sex ratio of founding populations can also influence invasion success in many different ways. Indeed, this aspect of propagule composition can influence population growth or spread (Lee et al., 2011; Miller & Inouye, 2011; Rankin & Kokko, 2007), and affect vulnerability to the Allee effect through demographic stochasticity (Bessa-Gomes et al., 2004; Lee et al., 2011). For example, female-biased propagules could positively affect population growth because the number of female gametes is positively related to offspring production (Dewsbury, 2005; Trivers, 1972). Alternatively, a female-biased population might be sperm limited and suffer reduced population-wide female mating success that could hinder population persistence by reducing fecundity and population growth (Gascoigne et al., 2009). Moreover, male-biased sex ratios can negatively affect population growth via reduced female survival, possibly due to male harassment of females (Le Galliard et al., 2005) or by increasing inbreeding (Aspbury et al., 2017; Rankin & Kokko, 2007; Rönn et al., 2006). Skewed propagule sex ratios may also reduce the efficacy of mate searching, which can slow or prevent establishment after introduction, though this effect may be small in polygynandrous species (Shaw et al., 2018). Importantly, the effects of male- or female-biased propagules could vary over time depending on the persistence of skews in the adult population sex ratio. Thus, biased propagule sex ratios could result in several different outcomes for introduced populations, which makes population-wide effects difficult to predict.

The dynamics of populations introduced to novel environments have been of broad interest to ecologists, evolutionary biologists and conservation biologists, and thus have been given considerable theoretical and empirical research attention (Lockwood et al., 2013). While the concepts described above are relatively well studied, we often do not have empirical data during early, transient stages of invasion because of the difficulty in identifying colonisation events (Lockwood et al., 2013; Marsico et al., 2010). Consequently, many studies take place after invasive
organisms are already established. Moreover, many introduced populations likely go extinct before being monitored, making it difficult to assess the role of propagule size or composition in the early stages of invasion (Dlugosch & Parker, 2008). In this study, we overcome some of these problems by using an experimental approach that manipulates founding populations of lizards on several small islands, which can provide realistic assessments of the role of propagule size and composition during the early stages of population establishment.

We aimed to identify factors that influence population growth during invasion by using spatially replicated experimental introductions of the brown anole (Anolis sagrei). This lizard species is native to Cuba and the Bahamas but invasive in the southeastern United States and elsewhere (Kolbe et al., 2004, 2007). Brown anoles produce a single egg approximately every 4-10 days from April to October (Mitchell et al., 2018; Pearson & Warner, 2018), but overall fecundity varies among females. For example, females can produce between 9-38 eggs (mean=25.7) over the reproductive season in the laboratory (Hall et al., 2020). This species has a polygynadrous mating system, and mating occurs continuously throughout the reproductive season. Additionally, females can store viable sperm from previous matings for at least two months (Calsbeek et al., 2007). Females are unlikely sperm-limited, but sperm limitation is plausible if adult sex ratios become heavily male biased. Mate competition among males can be strong (Tokarz, 1998) and there is indirect evidence of male avoidance by females, possibly due to costly mating interactions (Moon & Kamath, 2019). Individuals can reach sexual maturity within about five months of age, and the majority of adults typically die before reaching a second breeding season (Calsbeek, 2009; Cox & Calsbeek, 2010b). This species also has genotypic sex determination, suggesting balanced primary sex ratios (Cox et al., 2011; Urbach et al., 2013), yet adult population sex ratios can vary considerably (Schoener & Schoener, 1980). These aspects of
reproductive biology may have facilitated the invasion success of *A. sagrei*, and make this species well-suited for addressing the consequences of propagule size and sex ratio effects on population growth and establishment.

We manipulated propagule size and sex ratio on small islands, using founders from the nearby mainland. We then monitored population growth, survival of founders and their descendants, and population sex ratio over three generations. Our approach enabled us to describe the effects of propagule size and sex ratio on population demographics during establishment while controlling for propagule number and population density. We tested two predictions. First, we predicted decreased fecundity and population growth rate for the smallest propagules. Second, we expected patterns of female fecundity, survival and population growth to differ between populations with male- versus female-biased propagules. Specifically, in the first generation, we predicted high offspring production and population growth rate in populations with a female-biased propagule (due to greater numbers of females). While reduced per female fecundity is also plausible in female-biased populations due to sperm limitation, this is unlikely due to mating system of *A. sagrei*. For populations with male-biased propagules, we predict low fecundity and reduced population growth accompanied by reduced female survival (possibly due to sexual harassment). In subsequent generations, persistent male-biased sex ratios could generate a strong Allee effect, leading to low or negative population growth. In contrast, persistent female-biased sex ratios would lead to increased offspring production and continued population growth. To address these predictions, we also assess temporal variation in adult sex ratios and individual survival rates to provide additional insight into variation in offspring production and population growth.
137 **Methods**

138 *Study site*

139 Fieldwork took place on three islands at Tomoka State Park (TSP; Ormond Beach, FL,
140 USA) and six islands ~30 km north at the Guana Tolomato Matanzas National Estuarine Research
141 Reserve (GTM-NERR; Palm Coast, FL, USA; Table 1, Fig. 1). These islands were created between
142 1928 and 1970 from dredging the Intracoastal Waterway on the east coast of Florida (Baker, 2014).
143 The islands vary in shape, size, and the distribution of suitable habitats for *A. sagrei* (grass, shrubs,
144 palm and cedar trees) (Table 1, Fig. 1). We observed high densities of lizards during informal
145 surveys on the islands at TSP from 27-30 April 2009, suggesting that these islands can support
146 large populations of *A. sagrei* (Warner, pers. obs.). However, no lizards were found on these
147 islands two weeks prior to release; this was likely due to a flood during winter 2009. Informal
148 surveys at the GTM-NERR indicated that lizards were present on some islands, but in low
149 numbers; no lizards were observed when founders were released.

150 *Release and recapture*

151 In early spring 2011, adult lizards (431 males, 392 females) were collected from the
152 mainland near our field sites (St. Augustine, Palm Coast, and Ormond Beach, Florida). All lizards
153 were uniquely marked by toe clipping, measured (snout-vent length, SVL), weighed, and their sex
154 was identified. Lizards were then randomly assigned and released on one of nine islands (Table
155 1). Lizards released will be referred to as “founders” and other lizards are most likely descendants
156 of the founders. To test predictions related to propagule size, we manipulated propagule size by
157 adjusting the number of founders released to each island, keeping initial population density
158 between 0.06 to 0.08 lizards/m². This density is near the low end of the range recorded for other
159 *A. sagrei* populations (Calsbeek & Smith, 2007; Schoener & Schoener, 1980). Consequently,
propagule size ranged from 37 to 159 individuals (a 4.3-fold difference) among islands. To test predictions related to sex ratio, we manipulated the founding adult sex ratio to obtain 4 propagules (released on 4 islands) of different size with a female-biased sex ratio (0.33 male) and 5 propagules (released on 5 islands) of different size with a male-biased sex ratio (0.67 male) (Table 1). These sex ratios are within the range reported for natural populations (Schoener & Schoener, 1980), and our manipulations ensured that sex ratio was not confounded with propagule size. However, propagule treatments were not truly replicated in that each island population had a different number of founders; this is an inevitable outcome of using islands that differ in size and would be difficult, if not impossible, to overcome in any natural field experiment. Nevertheless, this approach provides a continuous range of propagule sizes that provides power to examine relationships between propagule size with offspring production and population growth after introduction while keeping propagule density constant.

Each island was resampled in August 2011, and then at the beginning (March/April) and end (October) of each subsequent reproductive season from October 2012 to April 2014 (Fig. 2; Table S1 for specific dates), resulting in seven capture-mark-recapture (CMR) events (including when islands were seeded with founders). We captured lizards by hand or with hand-held snare poles, individually placed them in a bag and recorded the nearest tree to each capture location (trees were labelled with forestry tags). We identified lizards by their toe clip and gave unmarked lizards (descendants) a unique toe clip. Consequently, a total of 5276 unique lizards (excluding founders) were obtained over the study on the 9 islands with 6407 captures or recaptures. We measured SVL for all individuals and classified females <34 mm and males <39 mm SVL as juveniles, and those larger as adults (Lee et al., 1989). We released all lizards within 24-48 hours at the tree nearest to their capture location. In October 2013 and April 2014, we permanently
removed lizards from the islands at TSP and deposited them in the Auburn University Museum of Natural History. While the study continued at the GTM-NERR, we terminated our population surveys on some islands at different times over the following three years.

### Statistical analyses

To test our two predictions, we needed to quantify temporal and among-island variation in population growth rate, fecundity, and individual survival rate. To do this, we first estimated population size and survival rate using parameterised multistate statistical models based on a conditional Arnason-Schwarz model (Schwarz et al., 1993). We defined four states: 1) individuals introduced on islands (Founder), 2) individuals that were descendants of the founders and their age defined as juvenile (Descendant Juvenile), 3) individuals that were descendants of the founders and their age defined as adult (Descendant Adult), or 4) individuals that were dead. The Arnason-Schwarz model estimates survival-transition probabilities (probability of being alive in the adult state at time t+1 if alive in the juvenile state at time t) along with detection probabilities (probability of capturing an individual in the adult state at time t). Survival-transition probabilities are decomposed into s, the probability that an individual present and alive at time t will be alive at time t+1, and Φ, the probability that an individual is in the adult state at time t+1 if alive in the juvenile state at time t (conditional of surviving between t and t+1) (Choquet, 2008; Schwarz et al., 1993). Our survival-transition probabilities refer to the probability of a descendant juvenile to survive and become a descendant adult (Fig. 2; Table S2), and this transition was only possible between October and March/April (i.e., winter). We sometimes recaptured individuals as juveniles in March/April (spring), but they were never re-encountered at following capture events. Because hatchlings produced early in the season (after our spring survey) could transition to adulthood before our fall survey, young adults could not be distinguished from older individuals that we did
not encounter in previous capture events. However, since only few individuals would transition to adulthood over the summer, we did not consider those individuals as potential juveniles.

*Estimation of detection probability and survival rate:* We estimated state-state transition probabilities with the E-SURGE program (Choquet et al., 2009), using relaxed parameters for survival and detection probabilities. We did this for two reasons: 1) transition and survival probabilities are difficult to distinguish from each other using capture/recapture events, and 2) estimating survival, transition, and detection probabilities within the same models can give poor estimates (Schwarz et al., 1993) and/or models may fail to converge. Based on convergent results from our preliminary models with relaxed parameters, we defined a fixed probability of 0.15 for descendant juveniles to remain descendant juveniles and a probability of 0.85 for descendant juveniles to become descendant adults. Transition parameters were entered into the E-SURGE program (Choquet et al., 2009), and we then ran models to estimate detection probability and survival rate. The models included time, population (i.e., island) and sex as covariates. These parameters were applied across populations and time periods.

We set estimated frequencies of each state to 1 for founders and 0 for descendants (juvenile and adult) for the introduction event (i.e., no descendants were present when founders were released). To account for variation in survival rate, we ran different models using time, state, sex and population as covariates, allowing two-way interactions among these variables. For detection probability, we tested time, sex and population as covariates, assuming equal detection probability among founders and descendants (juvenile or adult) (Rodda et al., 2015). Models were selected using Akaike information criteria (Anderson & Burnham, 2002). Additional details are in supplementary information (Table S2, S3).
229

**Estimation of offspring production, population growth and sex ratio:** We estimated the number of individuals at each capture event by dividing the number of individuals captured by the detection probability for that event (Table S1). To calculate offspring production, we divided the estimated number of juveniles (both sexes) at the end of the reproductive season ($j_t$) by the estimated number of adult females (founders and descendants) at the beginning of the reproductive season ($f_t$) (i.e., number of offspring produced over a breeding season and alive at the end of that season; offspring production = $j_t/f_t$). This value estimates the average number of offspring per female within each population based on all hatchlings that survived and were captured as juveniles. We used the estimated number of adults (founders and descendants of both sexes) at the beginning of each reproductive season (defined as $n_t$) to estimate annual population growth; annual population growth = ($n_{t+1} - n_t$)/$n_t$. Finally, we used the estimated number of adult males (founders and descendants) at the beginning of each reproductive season ($m_t$) and the estimated number of adult individuals ($n_t$) to estimate annual sex ratio; annual sex ratio = $m_t/n_t$. Because we permanently removed all lizards captured in October 2013 at TSP, we did not use data from the following capture event (April 2014) in any analysis except for estimating number of individuals. Likewise, we only estimated survival rate for descendant juveniles during the winter season.

246

**Testing predictions:** We used two linear mixed models (package *lme4* in R; Bates et al., 2015; R Core Team, 2020) to test our predictions of decreased fecundity and population growth rate in populations with a small propagule size. These models included propagule size, year, and their interaction as independent variables and offspring production or annual population growth as dependent variables; propagule size was standardised by subtracting the mean and dividing by the
standard deviation, and population was a random effect. We complemented this analysis with an additional test that only included the number of females released as the value for propagule size, since females can limit population growth.

To test our prediction that the direction of sex ratio bias will affect population growth or offspring production, we used Student t-tests to quantify differences between initially male- and female-biased populations in offspring production and population growth only for the first year of study. We limited this analysis to the first year of the study because initial biases in adult sex ratio did not persist over time and tended to be similar for initially male-biased and initially female-biased populations in 2012 and 2013 (see Results). To further investigate our hypothesis that propagule sex ratio can drive Allee Effects, we also quantified the effect of propagule sex ratio on survival rate with a linear mixed model. This model was fitted with a binomial distribution including survival rate from the two first time lapses (April to August 2011 and August 2011 to March 2012) as the dependent variable, and state, sex, propagule sex ratio, and two-way interactions (state x propagule sex ratio and sex x propagule sex ratio) as independent variables; population was a random effect.

Lastly, to integrate spatiotemporal variation in survival rate, model selection with Arnason-Schwarz models included time, state, sex, and population as covariates. Additionally, we used a linear mixed model fitted with a binomial distribution (package *lme4*) to quantify differences in survival rate among sexes and states; this model also included a two-way interaction between sex and state with population as a random effect.

## 273 Results

*Effect of propagule size on offspring production and population growth*
275 Offspring production was low during the first and second reproductive seasons (except for 276 one population), and then increased during the third reproductive season (Fig. 3a,b). Offspring 277 production was positively associated with propagule size (Wald χ²₁=6.36, p=0.012), but this 278 relationship varied slightly among years (Wald χ²₂=5.52, p=0.063); this relationship was positive 279 in 2012 and 2013 and slightly negative in 2011 (Fig. 3b). Annual population growth increased over 280 time (Wald χ²₁=5.28, p=0.022), but was not associated with propagule size (Wald χ²₁=1.26, 281 p=0.262) or the interaction between propagule size and year (Wald χ²₁=0.688, p=0.407) (Fig. 3c,d). 282 Results from analyses that used the number of females (rather than total number of females and 283 males) as propagule size showed similar patterns but with weaker statistical support. See 284 supplemental information for values for offspring production and population growth (Table S4) 285 and output from all statistical models (Tables S5-S8).

286 *Effect of propagule sex ratio on offspring production, population growth and survival rate*

287 Offspring production in the first year did not differ between populations with male-biased 288 propagules (mean=1.27 offspring/female) and those with female-biased propagules (mean=1.96 289 offspring/female) (Student t=0.829 (-1.315 - 2.691 95% CI, p=0.44) (Fig. 4a). Annual population 290 growth from 2011 to 2012 was lower in populations with male-biased propagules (mean growth 291 rate=-0.220) than those with female-biased propagules (mean growth rate=1.003) (Student t=2.463 292 (0.048 - 2.397 95%CI, p=0.043) (Fig. 4b). Four of the five populations with a male-biased 293 propagule had negative growth in the first year after founders were released whereas all 294 populations with female-biased propagules had positive annual growth during the first year, with 295 two of them more than doubling. In the second year, all populations had positive growth rates 296 regardless of propagule sex ratio (Fig. 3c). Survival rates of males and females over the first year 297 (i.e., from April 2011 to March 2012) were not affected by propagule sex ratio (sex*sex ratio:
298 Wald $\chi^2_1=0.558$, p=0.455, Table S9). All but one population shifted towards a balanced or female-biased sex ratio in the second year (Fig. 4c).
299

300

301

### *Variation in survival rate*

302 Survival rate varied considerably across time and islands, and differed among states
303 (founder, adult, juvenile) and sexes (Fig. 5, Table S10). Survival rates of founders was lower than
304 that of descendant adults (Wald $\chi^2_2=9.395$, p=0.009; Fig. 5a), and females had a higher survival
305 rate than males (Wald $\chi^2_1=6.807$, p=0.009) (Fig. 5a, Table S11). Survival rate varied among
306 populations depending upon sex, time, and state (Fig. 5b).

307

308

### Discussion

309 Our study aimed to understand how propagule size and sex ratio influence survival,
310 offspring production, and population growth during early stages of biological invasion. Consistent
311 with our first prediction, we found that offspring production remained low over three generations
312 for the smallest propagules, while it increased in larger propagules. However, this influence on
313 offspring production was not accompanied by an effect of propagule size on annual population
314 growth. Consistent with our second prediction, we found reduced population growth in initially
315 male-biased populations. However, we also predicted reduced female survival and offspring
316 production in male-biased populations; these predictions were not supported. Although
317 populations with female-biased propagules had increased annual growth in the first year after
318 introduction compared to those with male-biased propagules, this difference disappeared in the
319 following year, likely due to temporal shifts in adult sex ratio. Lastly, survival rates varied across
populations, time, and state, which can have important effects on population establishment in novel environments. Overall, these results show that different components of population fitness have different responses to propagule size and sex ratio in ways that likely affect the early stages of biological invasion.

### Propagule size

The Allee effect refers to a positive relationship between population growth rate and either density or population size, which puts small populations at higher risk of collapse than larger populations (Stephens et al., 1999). The low offspring production we observed on islands with a small founding propagule fits with this prediction. Notably, a population with one of the smallest propagules was on the verge of collapse (M2 Island) by the end of the study, whereas the other populations maintained (Table S1; Fig. S1). Interestingly, increased offspring production on islands with large founding propagules was generally not evident until the third generation. Although time lags in population growth are expected after introduction (e.g., Crooks & Soulé, 1999; Kelly et al., 2021; Rilov et al., 2004), we do not have a compelling explanation for why the time lag in offspring production was observed only for our large-propagule islands. This time lag may result from an unstable stage structure after introduction (indicative of the temporal variation we observed in survival; Fig. 5b), and increased offspring production from larger propagules may enable populations to reach a stable stage structure sooner than those founded by smaller propagules (Iles et al., 2016). These observations warrant further investigation, but regardless of the cause of this delayed effect, the positive increase in offspring production in populations with large propagules is consistent with a weak Allee effect.

Our results are consistent with past studies showing that populations founded from large propagules have increased offspring production (Bessa-Gomes et al., 2004; Blackburn et al., 2011;
Lockwood et al., 2013), but here we provide evidence of this pattern at a *per female* scale. While this result provides insight into the drivers of population growth after introduction, we do not know the mechanism responsible for the effect on reproduction. Nevertheless, we offer some possible explanations that warrant investigation. First, female fecundity might exhibit plasticity in response to population size (Karjalainen et al., 2016; Pârvulescu et al., 2015) or correlates of population size. High frequency of encounters with conspecifics may indicate that conditions are favourable for supporting large populations and females may increase their reproductive output accordingly; this reproductive plasticity might be exacerbated if food resources are abundant (Hall et al., 2018), and food abundance may positively correlate with population size. Such reproductive plasticity might facilitate a positive feedback loop and cause rapid population growth in newly introduced populations. Reduced offspring production per female on islands with small propagules may also be a signature of inbreeding depression, which is typically associated with small population size (Frankham et al., 2002). Alternatively, this pattern may reflect influences of factors that we could not control in our study, such as among-island variation in location, size, and habitat, or even the lack of replication of propagule size across islands.

Although anole populations with large founding propagules had increased offspring production, they did not exhibit increased population growth. This discrepancy could be generated by decreased recruitment to the adult stage (via juvenile survival or growth) in populations with large propagules. Additionally, survival and abundance varied considerably among populations, among states, and over time, which could reduce the likelihood of detecting a clear relationship between offspring production and population growth. Alternatively, this discrepancy may be the result of quantifying only one component of individual fitness (e.g., offspring production in our case) rather than the sum all possible fitness components (Stephens et al., 1999). Additionally,
Allee effects on a component of fitness might be compensated by demographic factors (e.g., number of breeding females, high fecundity rate: Bessa-Gomes et al., 2004), which could lead to a weak demographic Allee effect on population growth, and thus a lower probability for population extinction (Stephens et al., 1999).

The high fecundity of *A. sagrei* over a long reproductive season (Lee et al., 1989) and their "fast" life-history strategy may explain the successful establishment of populations (Allen et al., 2017; Fetters & McGlothlin, 2017). Indeed, populations derived from small propagules may experience an early lag in growth, but their probability of collapse might be reduced by high fecundity. This is supported by previous experimental introductions of *A. sagrei* to islands using considerably smaller propagule sizes than those used in our study. For example, populations persisted on 18 small islands (of 24) in the Bahamas with only 5 to 10 founders (Losos et al., 1997; Schoener & Schoener, 1983). Other introductions of brown anoles involved 18 and 19 founding individuals on two small islands (~0.09 lizards/m²); both populations quickly established (Campbell & Echternacht, 2003) and remained present for two decades (Stuart et al., 2014). In addition, releases of male/female pairs on small islands in the Bahamas resulted in positive increases in population size over two years followed by considerable among-island variation in population fluctuation (Kolbe et al., 2012). Thus, populations with small propagules can persist over long periods, but they are more vulnerable to founder (Kolbe et al., 2012) and Allee effects (Bessa-Gomes et al., 2004) and may do poorly over longer periods. Indeed, most populations in the Kolbe et al. (2012) study exhibited little to negative growth after the second year, and some had <10 individuals four years after release. Such patterns may have been driven by decreased offspring production due to small founding propagules as observed on some islands in our study. Nevertheless, our results provide additional empirical support that organisms with "fast" life-
389 history traits can overcome the negative consequences of small propagule size and successfully establish in novel environments (Allen et al., 2017).
390

391

392 *Propagule sex ratio*

393 Propagule sex ratio had no effect on offspring production. Although low offspring production is expected in the first reproductive season regardless of the direction of founder sex ratio bias (Bessa-Gomes et al., 2004; Boukal & Berec, 2002), we predicted offspring production to remain low over subsequent generations on islands with male-biased propagules, but to increase on islands with female-biased propagules if biased sex ratios persisted over time. These expected patterns were not observed in our experiment, likely because the adult sex ratios on most islands shifted toward 1:1 (or slight female bias) after the first year (Fig. 4c). The lack of an effect of propagule sex ratio on offspring production in the first reproductive season could have been due to sperm storage from matings prior to introduction and high male copulation rate (Calsbeek et al., 2007; Kahrl & Cox, 2015). The rapid shift in adult sex ratio after introduction was likely related to high fecundity and short life span in our study species. Additionally, these life-history characteristics of anoles, coupled with their genotypic sex determining mechanism (Rovatsos et al., 2014) and their limited capacity to adjust offspring sex ratios (Warner, unpubl. data; Urbach et al., 2013; but see Cox & Calsbeek, 2010a), could cause rapid shifts towards balanced population sex ratios. The polygynandrous mating system of *A. sagrei* may also facilitate population persistence even when propagule sex ratio is heavily skewed (Shaw et al., 2018). Overall, a range of reproductive and demographic factors can rapidly drive population sex ratios toward 1:1 even if the propagule is heavily sex-biased, which may contribute to the establishment of this invasive species.
400
401
402
403
404
405
406
407
408
409
410
411
Although offspring production was not influenced by founder sex ratio, islands with female-biased propagules exhibited greater annual growth in the first year than those with male-biased propagules (Fig. 4b). Indeed, population growth on islands with male-biased propagules was mostly negative over the first year. Male-biased sex ratios can negatively affect hatchling/juvenile survival (Delaney & Warner, 2017), which could reduce annual population growth, yet differences in juvenile survival rate between initially male- and female-biased populations were not evident in our data (Table S9). Sexual conflict could also be higher in male-biased populations, and may reduce female fertility and survival rate, thus decrease population growth. Yet, the lack of difference in offspring production between initially female- and male-biased populations does not support sexual conflict as a potential explanation for the difference in annual growth. Likewise, our prediction of low female survival in male-biased populations (due to sexual harassment: Le Galliard et al., 2005; cannibalism: Reedy et al., 2013) was not supported. Instead, females consistently had higher survival than males regardless of propagule sex ratio and state (founders, descendant juveniles and adults) (Tables S9, S10, S11). Other studies have also shown females to have similar or higher survival than males (Cox et al., 2010; Delaney & Warner, 2016; Schoener & Schoener, 1982). A 0.67 propagule sex ratio was enough to detect a difference in annual population growth, but a greater bias may be necessary to affect offspring production or female survival rate (Bessa-Gomes et al., 2004). Annual population growth was positive during the second year after introduction regardless of propagule sex ratio (Fig. 3c). The short life-span of *A. sagrei*, which contributes to a rapid shift in adult sex ratio, likely influenced initially male-biased populations to rebound from the initial negative annual growth. Consequently, initially-male biased populations may have slower colonisation dynamics but equal chance of colonising success.
435

436

Variation in survival rate

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

We show substantial variation in survival rate across islands, time, and states (Fig. 5b), which may be explained by biological and logistical factors. For example, habitat structure, predation, and prey vary across islands and over time, which could impact how individuals use habitat (Calsbeek & Irschick, 2007; Delaney & Warner, 2016). Additionally, variation in survival may reflect demographic stochasticity of an unstable population structure during early, transient periods of establishment (Iles et al., 2016; Stott et al., 2011). Indeed, estimated population sizes also show considerable temporal fluctuations across islands (Fig. S1). For logistical reasons, as previously noted, populations were not truly replicated because each population had a different combination of propagule size and sex-ratio treatment, which could contribute to the variation observed. Moreover, detectability was relatively low on some islands and in some cases had large confidence intervals (Fig. S2), which could give rise to high spatial and temporal variation in survival and population estimates. Nevertheless, effects of our propagule treatments were still detectable despite these logistical caveats. Consequently, we are confident that our study provides biological insight into the role of propagule pressure on population establishment in wild populations.

452

453

454

455

456

457

The variation that we observed among states (i.e., founders, descendant juveniles and adults) has important implications for establishment success. For instance, founders (by definition) relocate outside their home ranges, and thus must acclimate to new habitat and compete for new territories. Consequently, reduced founder survival immediately after introduction is expected (Calsbeek & Irschick, 2007), and we show that founders had lower survival than the juvenile and adult descendants that were produced on the islands (Fig. 5a). Similar patterns have been observed
in relocation and restoration studies (Hardman & Moro, 2006; Jenni et al., 2015) and may be associated with stress induced by being in unfamiliar areas (Armstrong & Seddon, 2008; Dickens et al., 2010). Importantly, one caveat for experimental studies like ours is that founders are randomly drawn from a source population and forced to disperse, but there may be non-random subsets of individuals dispersing into novel areas during natural or accidental invasions. Although *A. sagrei* populations seem resilient to reduced founder survival, this could have large negative impacts on population establishment in other species (particularly those with slow life history strategies or longer reproductive lifespans; Capellini et al., 2015; Sol et al., 2012), and is important for managers to consider in restoration and reintroduction efforts.

## Conclusions

Our results support the prediction that populations founded by a small propagule experience lower offspring production than those founded by a large propagule, but this effect did not contribute to annual population growth. We observed an effect of propagule sex ratio on population growth during the first year, with a negative annual growth for populations founded by male-biased propagules. However, the rapid shifts towards balanced sex ratios lead to similar annual growth between initially male- and female-biased populations in the second year after introduction. Despite the effects of propagule size and sex ratio, the life-history traits of *A. sagrei* (e.g., short life span, high fecundity) likely enabled populations to overcome Allee effects and enabled all populations to persist over the duration of this study. These results have broad implications for understanding the early stages of biological invasion. Indeed, different demographic factors of founding populations can have varied impacts on offspring production and population growth, and the consequences manifest at different times after introduction. Our rare experimental test of
propagule pressure can also inform predictions of colonisation dynamics in response to different compositions of founding populations, which is critical in the context of reintroduction programs and invasion dynamics.

484

## 485 Acknowledgements

Thanks to the Eppley Foundation for Research, National Geographic Society, the Waitt Foundation, the American Philosophical Society, the University of Alabama at Birmingham, and Auburn University for funding this research. We are grateful to Alexis Harrison helping initiate this work and numerous undergraduate students for assistance in the field. This research was approved by the University of Alabama at Birmingham Institutional Animal Care and Use Committee (protocol: 130709913), the FL Department of Environmental Protection (permit# 12111213), and the Guana Tolomato Matanzas National Estuarine Research Reserve. This is manuscript #918 of the Auburn University Museum of Natural History. We declare no conflicts of interest.

495
**Authors' contributions:** DAW and AMR designed the research. AMR, AK, TSM, AMD, DMD, PRP, RMC and DAW collected data. AF performed statistical analyses and wrote the first version of the manuscript, and all authors contributed substantially to revisions of the manuscript.

498
**Data availability statement:** Under the condition of manuscript acceptance, data will be archived as supplementary information.

500

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718 **Tables and Figures**

719 **Table 1.** Description of the islands and founding populations at Tomoka State Park (TSP) and the 720 Guana Tolomato Matanzas National Estuarine Research Reserve (GTM-NERR). Founders were 721 captured between 29 March and 13 April 2011. Numbers of individuals of each sex released are 722 in brackets [number of males: number of females]. The F and M for the island names refer to 723 female-biased and male-biased, respectively.

724 **Figure 1.** a) Location of the sites located in Florida. b) The six islands in the Guana Tolomato 725 Matanzas National Estuarine Research Reserve (GTM-NERR, 29'6" N, 81'2" W). c) The three 726 islands in Tomoka State Park (TSP, 29'3" N, 81'1" W). The F and M for the island names refers 727 to female-biased and male-biased, respectively. All islands are isolated from the mainland, but 728 islands M5 and M4 at the GTM-NERR, and M1 and M2 at TSP are occasionally connected at low 729 tide (we never detected evidence of lizards moving between islands). Copyright Google Earth 2020 730 (Images from January 2012).

731 **Figure 2.** Seasonal timeline illustrating the reproductive season (light blue shading), non-732 reproductive season (light green) and state transitions. All populations were founded in April 2011. 733 Populations were censused in August 2011, and then in April and October thereafter (denoted by 734 the rectangular boxes). All non-adult lizards captured were classified as descendant juveniles that 735 hatched sometime during the hatching season in that same year; those that were recaptured the 736 following spring transitioned to the descendant adult state.

737 **Figure 3.** Influence of propagule size on offspring production and annual growth across years for 738 each population. a) The effects of year and propagule size on offspring production. The dotted line
delimits a value of two juveniles produced per female, which would double population size when every individual survived. b) Relationships between propagule size and offspring production for each year (the 2013 relationship was statistically supported). c) The effects of growth period and propagule size on annual population growth. The dashed line delimits a null growth (an unchanged estimated number of individuals), and the dotted line delimits a growth rate of 1 (doubling of the estimated number of individuals). d) Relationships between propagule size and population growth for each period (relationships were not statistically supported). In all graphs, propagule size is a continuous variable, and the size of the points is proportional to propagule size.

**Figure 4.** Box plots of a) offspring production in 2011 (year of propagule release) for populations with male- and female-biased propagules (p=0.44); b) annual growth in 2011-2012 for populations with male- and female-biased propagules (p=0.043). c) Shifts in population sex ratio among years on each island. The bold dashed line delimits a balanced sex ratio (1:1). The dotted lines delimit a 2:1 male-biased sex ratio (y = 0.67) and a 1:2 female-biased sex ratio (y = 0.33). The size of the point is proportional to propagule size. Estimated values are available in Table S5.

**Figure 5.** Survival estimates across nine islands for each state and sex across time (squares denote females, circles denote males). a) Parameter estimates across islands and time for males and females. Error bars represent 95% confidence intervals. b) Mean parameter estimates for founders, adults and juveniles of each sex. Error bars represent the standard deviation. Values for each island are in Table S11. The x-axis refers to time across reproductive seasons (RS, from April to October) and non-reproductive seasons (non-RS, from October to April) from 2011-2013. Islands are ordered from small to large propagule size in the same order as in Table 1. No descendant adults were found in fall 2011 on island M2; thus, there was no value for survival rate on this island during non-reproductive season 2011-2012. Values are available in Table S1.
<table><caption>**Table 1.** Description of the islands and founding populations at Tomoka State Park (TSP) and the Guana Tolomato Matanzas National Estuarine Research Reserve (GTM-NERR). Founders were captured between 29 March and 13 April 2011. Numbers of individuals of each sex released are in brackets [number of males: number of females]. The F and M for the island names refer to female-biased and male-biased, respectively.</caption><thead><tr><th>Island</th><th>Location</th><th>Area (m²)</th><th>Date of release</th><th>Propagule size</th><th>Sex-ratio bias</th><th>Density (individuals/m²)</th></tr></thead><tbody><tr><td>F1</td><td>TSP</td><td>450</td><td>14 April 2011</td><td>37 [11:26]</td><td>Female</td><td>0.082</td></tr><tr><td>F2</td><td>GTM-NERR</td><td>1200</td><td>13 April 2011</td><td>73 [25:48]</td><td>Female</td><td>0.061</td></tr><tr><td>F3</td><td>GTM-NERR</td><td>1700</td><td>13 April 2011</td><td>111 [36:75]</td><td>Female</td><td>0.065</td></tr><tr><td>F4</td><td>GTM-NERR</td><td>1920</td><td>13 April 2011</td><td>121 [40:81]</td><td>Female</td><td>0.063</td></tr><tr><td>M1</td><td>TSP</td><td>711</td><td>14 April 2011</td><td>45 [30:15]</td><td>Male</td><td>0.063</td></tr><tr><td>M2</td><td>TSP</td><td>840</td><td>14 April 2011</td><td>51 [33:18]</td><td>Male</td><td>0.061</td></tr><tr><td>M3</td><td>GTM-NERR</td><td>1720</td><td>13 April 2011</td><td>106 [70:36]</td><td>Male</td><td>0.062</td></tr><tr><td>M4</td><td>GTM-NERR</td><td>1970</td><td>13 April 2011</td><td>120 [82:38]</td><td>Male</td><td>0.061</td></tr><tr><td>M5</td><td>GTM-NERR</td><td>2680</td><td>13 April 2011</td><td>159 [104:55]</td><td>Male</td><td>0.059</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>**Figure 1.** a) Location of the sites located in Florida. b) The six islands in the Guana Tolomato Matanzas National Estuarine Research Reserve (GTM-NERR, 29'6" N, 81'2" W). c) The three islands in Tomoka State Park (TSP, 29'3" N, 81'1" W). The F and M for the island names refers to female-biased and male-biased, respectively. All islands are isolated from the mainland, but islands M5 and M4 at the GTM-NERR, and M1 and M2 at TSP are occasionally connected at low tide (we never detected evidence of lizards moving between islands). Copyright Google Earth 2020 (Images from January 2012).</figcaption></figure>
<figure><img src="image_4.png" /></figure>

**Figure 2.** Seasonal timeline illustrating the reproductive season (light blue shading), non-reproductive season (light green) and state transitions. All populations were founded in April 2011. Populations were censused in August 2011, and then in April and October thereafter (denoted by the rectangular boxes). All non-adult lizards captured were classified as descendant juveniles that hatched sometime during the hatching season in that same year; those that were recaptured the following spring transitioned to the descendant adult state.
<figure><img src="image_5.png" /></figure>

**Figure 3.** Influence of propagule size on offspring production and annual growth across years for each population. a) The effects of year and propagule size on offspring production. The dotted line delimits a value of two juveniles produced per female, which would double population size when every individual survived. b) Relationships between propagule size and offspring production for each year (the 2013 relationship was statistically supported). c) The effects of growth period and propagule size on annual population growth. The dashed line delimits a null growth (an unchanged estimated number of individuals), and the dotted line delimits a growth rate of 1 (doubling of the estimated number of individuals). d) Relationships between propagule size and population growth for each period (relationships were not statistically supported). In all graphs, propagule size is a continuous variable, and the size of the points is proportional to propagule size.
<figure><img src="image_6.png" /></figure>

**Figure 4.** Box plots of a) offspring production in 2011 (year of propagule release) for populations with male- and female-biased propagules (p=0.44); b) annual growth in 2011-2012 for populations with male- and female-biased propagules (p=0.043). c) Shifts in population sex ratio among years on each island. The bold dashed line delimits a balanced sex ratio (1:1). The dotted lines delimit a 2:1 male-biased sex ratio (y = 0.67) and a 1:2 female-biased sex ratio (y = 0.33). The size of the point is proportional to propagule size. Estimated values are available in Table S5.
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 5.** Survival estimates across nine islands for each state and sex across time (squares denote females, circles denote males). a) Parameter estimates across islands and time for males and females. Error bars represent 95% confidence intervals. b) Mean parameter estimates for founders, adults and juveniles of each sex. Error bars represent the standard deviation. Values for each island are in Table S11. The x-axis refers to time across reproductive seasons (RS, from April to October) and non-reproductive seasons (non-RS, from October to April) from 2011-2013. Islands are ordered from small to large propagule size in the same order as in Table 1. No descendant adults were found in fall 2011 on island M2; thus, there was no value for survival rate on this island during non-reproductive season 2011-2012. Values are available in Table S1.</figcaption></figure>
**Propagule size and sex ratio influence colonisation dynamics after experimental introduction of a non-native lizard - Supplementary information**

Amélie Fargevieille<fnref n="1" />, Aaron M. Reedy<fnref n="1" />,<fnref n="2" />, Ariel Kahrl<fnref n="2" />,<fnref n="3" />, Timothy S. Mitchell<fnref n="1" />,<fnref n="4" />,<fnref n="5" />, Andrew M. Durso<fnref n="6" />, David M. Delaney<fnref n="4" />,<fnref n="7" />, Phillip R. Pearson<fnref n="1" />,<fnref n="7" />,<fnref n="8" />, Robert M. Cox<fnref n="2" />, Daniel A. Warner<fnref n="1" />,<fnref n="4" />,<fnref n="7" />

<footnote n="1">¹Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA</footnote>

<footnote n="2">²Department of Biology, University of Virginia, Charlottesville, VA 22904, USA</footnote>

<footnote n="3">³Department of Zoology/Ethology, Stockholm University, Svante Arrhenius väg 18B SE-10691, Stockholm, Sweden</footnote>

<footnote n="4">⁴Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011, USA</footnote>

<footnote n="5">⁵Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota 55108, USA</footnote>

<footnote n="6">⁶Department of Biological Sciences, Florida Gulf Coast University, Fort Myers, FL 33965, USA</footnote>

<footnote n="7">⁷Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA</footnote>

<footnote n="8">⁸Centre for Conservation, Ecology and Genomics, University of Canberra, Bruce, ACT 2617, Australia</footnote>
<table><caption>**Table S1 - Island F1:** State- and sex-specific values extracted from Capture-Mark-Recapture analyses for Island F1. "Detectability" refers to the estimated detection probability (with 95% confidence intervals in brackets) on the island; "Count" refers to the number of individuals (by state and sex) captured during the period of capture ("-" denotes that no individual was found); "Estimated abundance" refers to the estimated number of individuals (by state and sex) on the island (with 95% confidence intervals) and corresponds to "Count" divided by "Detectability" (with "-" indicating the inability to estimate the abundance); "Survival rate" refers to the survival probability (by state and sex) (with 95% confidence intervals in brackets) on the island estimated during the corresponding time lap.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">State</th><th colspan="2">Founders</th><th colspan="2">Descendant juveniles</th><th colspan="2">Descendant adults</th></tr><tr><th>Sex</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td rowspan="3">Spring 2011</td><td rowspan="3">April 13 to April 14 2011</td><td>Detectability</td><td colspan="8">1 (1-1)</td></tr><tr><td>Count</td><td>11</td><td>26</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Reproductive season 2011</td><td rowspan="2"></td><td>Survival Rate</td><td>0.03<br>(0.00-0.12)</td><td>0.07<br>(0.02-0.25)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Detectability</td><td colspan="8">0.92 (0.70-0.98)</td></tr><tr><td rowspan="3">Fall 2011</td><td rowspan="3">August 10 to August 24 2011</td><td>Count</td><td>0</td><td>2</td><td>17</td><td>20</td><td>3</td><td>4</td></tr><tr><td>Estimated abundance</td><td>-</td><td>2<br>(2-3)</td><td>18<br>(17-24)</td><td>22<br>(20-29)</td><td>3<br>(3-4)</td><td>4<br>(4-6)</td></tr><tr><td>Survival Rate</td><td>-</td><td>-</td><td>0.28<br>(0.13-0.50)</td><td>0.54<br>(0.25-0.80)</td><td>0.05<br>(0.01-0.23)</td><td>0.14<br>(0.04-0.39)</td></tr><tr><td rowspan="3">Spring 2012</td><td rowspan="3">March 28 to April 11 2012</td><td>Detectability</td><td colspan="8">0.76 (0.41-0.94)</td></tr><tr><td>Count</td><td>0</td><td>0</td><td>-</td><td>-</td><td>18</td><td>33</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>24<br>(19-44)</td><td>43<br>(35-80)</td></tr><tr><td rowspan="2">Reproductive season 2012</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.10<br>(0.03-0.26)</td><td>0.25<br>(0.12-0.43)</td></tr><tr><td>Detectability</td><td colspan="8">0.69 (0.34-0.91)</td></tr><tr><td rowspan="3">Fall 2012</td><td rowspan="3">September 29 to October 8 2012</td><td>Count</td><td>0</td><td>0</td><td>36</td><td>24</td><td>42</td><td>29</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>52<br>(40-106)</td><td>35<br>(26-71)</td><td>61<br>(46-124)</td><td>42<br>(32-85)</td></tr><tr><td>Survival Rate</td><td>-</td><td>-</td><td>0.68<br>(0.15-0.96)</td><td>0.86<br>(0.17-0.99)</td><td>0.51<br>(0.17-0.84)</td><td>0.76<br>(0.21-0.97)</td></tr><tr><td rowspan="3">Spring 2013</td><td rowspan="3">March 16 to March 28 2013</td><td>Detectability</td><td colspan="8">0.59 (0.24-0.87)</td></tr><tr><td>Count</td><td>0</td><td>0</td><td>-</td><td>-</td><td>58</td><td>61</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>98<br>(67-238)</td><td>103<br>(70-250)</td></tr><tr><td rowspan="2">Reproductive season 2013</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.05<br>(0.02-0.13)</td><td>0.14<br>(0.06-0.30)</td></tr><tr><td>Detectability</td><td colspan="8">0.50 (0.19-0.82)</td></tr><tr><td rowspan="3">Fall 2013</td><td rowspan="3">October 2 to October 15 2013</td><td>Count</td><td>0</td><td>0</td><td>39</td><td>30</td><td>25</td><td>29</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>77<br>(48-210)</td><td>60<br>(37-161)</td><td>58<br>(35-156)</td><td>50<br>(31-135)</td></tr></tbody></table>
<table><caption>**Table S1 - Island F2:** State- and sex-specific values extracted from Capture-Mark-Recapture analyses for Island F2. "Detectability" refers to the estimated detection probability (with 95% confidence intervals in brackets) on the island; "Count" refers to the number of individuals (by state and sex) captured during the period of capture ("-" denotes that no individual was found); "Estimated abundance" refers to the estimated number of individuals (by state and sex) on the island (with 95% confidence intervals) and corresponds to "Count" divided by "Detectability" (with "-" indicating the inability to estimate the abundance); "Survival rate" refers to the survival probability (by state and sex) (with 95% confidence intervals in brackets) on the island estimated during the corresponding time lap.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">State</th><th colspan="2">Founders</th><th colspan="2">Descendant juveniles</th><th colspan="2">Descendant adults</th></tr><tr><th>Sex</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td rowspan="3">Spring 2011</td><td rowspan="3">April 13 to April 14 2011</td><td>Detectability</td><td></td><td></td><td colspan="4">1 (1-1)</td></tr><tr><td>Count</td><td>25</td><td>48</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Reproductive season 2011</td><td rowspan="2"></td><td>Survival Rate</td><td>0.13<br>(0.06-0.27)</td><td>0.31<br>(0.18-0.49)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Detectability</td><td></td><td></td><td colspan="4">0.74 (0.52-0.88)</td></tr><tr><td rowspan="3">Fall 2011</td><td rowspan="3">August 10 to August 24 2011</td><td>Count</td><td>1</td><td>13</td><td>61</td><td>73</td><td>19</td><td>27</td></tr><tr><td>Estimated abundance</td><td>1<br>(1-2)</td><td>18<br>(15-25)</td><td>82<br>(69-118)</td><td>99<br>(83-142)</td><td>26<br>(21-37)</td><td>36<br>(31-52)</td></tr><tr><td>Survival Rate</td><td>-</td><td>0.06<br>(0-0.52)</td><td>0.08<br>(0.03-0.17)</td><td>0.20<br>(0.11-0.34)</td><td>0.02<br>(0.01-0.10)</td><td>0.04<br>(0.02-0.21)</td></tr><tr><td rowspan="3">Spring 2012</td><td rowspan="3">March 28 to April 11 2012</td><td>Detectability</td><td></td><td></td><td colspan="4">0.44 (0.26-0.64)</td></tr><tr><td>Count</td><td>0</td><td>1</td><td>-</td><td>-</td><td>10</td><td>29</td></tr><tr><td>Estimated abundance</td><td>-</td><td>2<br>(2-4)</td><td>-</td><td>-</td><td>23<br>(16-38)</td><td>65<br>(45-110)</td></tr><tr><td rowspan="2">Reproductive season 2012</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.56<br>(0.14-0.91)</td><td>0.79<br>(0.33-0.97)</td></tr><tr><td>Detectability</td><td></td><td></td><td colspan="4">0.36 (0.20-0.55)</td></tr><tr><td rowspan="3">Fall 2012</td><td rowspan="3">September 29 to October 8 2012</td><td>Count</td><td>0</td><td>0</td><td>15</td><td>5</td><td>15</td><td>39</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>42<br>(27-74)</td><td>14<br>(9-25)</td><td>64<br>(41-113)</td><td>108<br>(70-191)</td></tr><tr><td>Survival Rate</td><td>-</td><td>-</td><td>0.16<br>(0.04-0.44)</td><td>0.36<br>(0.12-0.70)</td><td>0.16<br>(0.05-0.38)</td><td>0.36<br>(0.16-0.63)</td></tr><tr><td rowspan="3">Spring 2013</td><td rowspan="3">March 16 to March 28 2013</td><td>Detectability</td><td></td><td></td><td colspan="4">0.27 (0.14-0.45)</td></tr><tr><td>Count</td><td>0</td><td>0</td><td>-</td><td>-</td><td>17</td><td>21</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>63<br>(38-121)</td><td>78<br>(47-149)</td></tr><tr><td rowspan="2">Reproductive season 2013</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.33<br>(0.09-0.71)</td><td>0.60<br>(0.23-0.88)</td></tr><tr><td>Detectability</td><td></td><td></td><td colspan="4">0.20 (0.10-0.36)</td></tr><tr><td rowspan="3">Fall 2013</td><td rowspan="3">October 2 to October 15 2013</td><td>Count</td><td>0</td><td>0</td><td>22</td><td>21</td><td>91</td><td>57</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>108<br>(61-214)</td><td>104<br>(58-205)</td><td>449<br>(252-887)</td><td>281<br>(158-555)</td></tr></tbody></table>
<table><caption>**Table S1 - Island F3:** State- and sex-specific values extracted from Capture-Mark-Recapture analyses for Island F3. "Detectability" refers to the estimated detection probability (with 95% confidence intervals in brackets) on the island; "Count" refers to the number of individuals (by state and sex) captured during the period of capture ("-" denotes that no individual was found); "Estimated abundance" refers to the estimated number of individuals (by state and sex) on the island (with 95% confidence intervals) and corresponds to "Count" divided by "Detectability" (with "-" indicating the inability to estimate the abundance); "Survival rate" refers to the survival probability (by state and sex) (with 95% confidence intervals in brackets) on the island estimated during the corresponding time lap.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">State</th><th colspan="2">Founders</th><th colspan="2">Descendant juveniles</th><th colspan="2">Descendant adults</th></tr><tr><th>Sex</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td rowspan="3">Spring 2011</td><td rowspan="3">April 13 to April 14 2011</td><td>Detectability</td><td></td><td></td><td colspan="4">1 (1-1)</td></tr><tr><td>Count</td><td>36</td><td>75</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Reproductive season 2011</td><td rowspan="2"></td><td>Survival Rate</td><td>0.12<br>(0.07-0.20)</td><td>0.26<br>(0.18-0.37)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Detectability</td><td></td><td></td><td colspan="4">0.84 (0.72-0.91)</td></tr><tr><td rowspan="3">Fall 2011</td><td rowspan="3">August 10 to August 24 2011</td><td>Count</td><td>6</td><td>12</td><td>52</td><td>56</td><td>21</td><td>41</td></tr><tr><td>Estimated abundance</td><td>7<br>(7-8)</td><td>14<br>(13-17)</td><td>62<br>(52-72)</td><td>67<br>(61-77)</td><td>25<br>(23-29)</td><td>49<br>(45-57)</td></tr><tr><td>Survival Rate</td><td>-</td><td>0.44<br>(0.24-0.68)</td><td>0.21<br>(0.13-0.33)</td><td>0.41<br>(0.28-0.55)</td><td>0.18<br>(0.10-0.32)</td><td>0.36<br>(0.22-0.53)</td></tr><tr><td rowspan="3">Spring 2012</td><td rowspan="3">March 28 to April 11 2012</td><td>Detectability</td><td></td><td></td><td colspan="4">0.60 (0.47-0.70)</td></tr><tr><td>Count</td><td>0</td><td>6</td><td>-</td><td>-</td><td>59</td><td>78</td></tr><tr><td>Estimated abundance</td><td>-</td><td>10<br>(8-13)</td><td>-</td><td>-</td><td>99<br>(83-124)</td><td>131<br>(110-164)</td></tr><tr><td rowspan="2">Reproductive season 2012</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>0.53<br>(0.23-0.81)</td><td>-</td><td>-</td><td>0.21<br>(0.13-0.31)</td><td>0.40<br>(0.28-0.52)</td></tr><tr><td>Detectability</td><td></td><td></td><td colspan="4">0.51 (0.40-0.62)</td></tr><tr><td rowspan="3">Fall 2012</td><td rowspan="3">September 29 to October 8 2012</td><td>Count</td><td>0</td><td>2</td><td>38</td><td>40</td><td>50</td><td>78</td></tr><tr><td>Estimated abundance</td><td>-</td><td>4<br>(3-5)</td><td>74<br>(61-94)</td><td>78<br>(65-99)</td><td>98<br>(81-124)</td><td>152<br>(126-193)</td></tr><tr><td>Survival Rate</td><td>-</td><td>0.78<br>(0.24-0.97)</td><td>0.15<br>(0.08-0.26)</td><td>0.30<br>(0.18-0.47)</td><td>0.33<br>(0.22-0.48)</td><td>0.56<br>(0.42-0.69)</td></tr><tr><td rowspan="3">Spring 2013</td><td rowspan="3">March 16 to March 28 2013</td><td>Detectability</td><td></td><td></td><td colspan="4">0.40 (0.32-0.50)</td></tr><tr><td>Count</td><td>0</td><td>1</td><td>-</td><td>-</td><td>59</td><td>95</td></tr><tr><td>Estimated abundance</td><td>-</td><td>2<br>(2-3)</td><td>-</td><td>-</td><td>146<br>(118-186)</td><td>235<br>(191-300)</td></tr><tr><td rowspan="2">Reproductive season 2013</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.51<br>(0.31-0.71)</td><td>0.73<br>(0.54-0.86)</td></tr><tr><td>Detectability</td><td></td><td></td><td colspan="4">0.32 (0.24-0.41)</td></tr><tr><td rowspan="3">Fall 2013</td><td rowspan="3">October 2 to October 15 2013</td><td>Count</td><td>0</td><td>0</td><td>318</td><td>277</td><td>125</td><td>103</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>992<br>(779-1302)</td><td>864<br>(679-1135)</td><td>390<br>(306-512)</td><td>321<br>(252-422)</td></tr></tbody></table>
<table><caption>**Table S1 - Island F4:** State- and sex-specific values extracted from Capture-Mark-Recapture analyses for Island F4. "Detectability" refers to the estimated detection probability (with 95% confidence intervals in brackets) on the island; "Count" refers to the number of individuals (by state and sex) captured during the period of capture ("-" denotes that no individual was found); "Estimated abundance" refers to the estimated number of individuals (by state and sex) on the island (with 95% confidence intervals) and corresponds to "Count" divided by "Detectability" (with "-" indicating the inability to estimate the abundance); "Survival rate" refers to the survival probability (by state and sex) (with 95% confidence intervals in brackets) on the island estimated during the corresponding time lap.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">State</th><th colspan="2">Founders</th><th colspan="2">Descendant juveniles</th><th colspan="2">Descendant adults</th></tr><tr><th>Sex</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td rowspan="3">Spring 2011</td><td rowspan="3">April 13 to April 14 2011</td><td>Detectability</td><td></td><td></td><td colspan="2">1 (1-1)</td><td></td><td></td></tr><tr><td>Count</td><td>40</td><td>81</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Reproductive season 2011</td><td rowspan="2"></td><td>Survival Rate</td><td>0.45<br>(0.34-0.57)</td><td>0.50<br>(0.39-0.61)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Fall 2011</td><td rowspan="3">August 10 to August 24 2011</td><td>Detectability</td><td></td><td></td><td colspan="2">0.78 (0.65-0.87)</td><td></td><td></td></tr><tr><td>Count</td><td>18</td><td>31</td><td>26</td><td>26</td><td>8</td><td>16</td></tr><tr><td>Estimated abundance</td><td>23<br>(21-27)</td><td>40<br>(36-47)</td><td>33<br>(30-40)</td><td>33<br>(30-40)</td><td>10<br>(9-12)</td><td>20<br>(18-24)</td></tr><tr><td rowspan="2">Non-Reproductive season 2011-2012</td><td rowspan="2"></td><td>Survival Rate</td><td>0.67<br>(0.39-0.86)</td><td>0.71<br>(0.44-.89)</td><td>0.48<br>(0.30-0.66)</td><td>0.53<br>(0.34-0.71)</td><td>0.79<br>(0.52-0.93)</td><td>0.83<br>(0.57-0.94)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Spring 2012</td><td rowspan="3">March 28 to April 11 2012</td><td>Detectability</td><td></td><td></td><td colspan="2">0.50 (0.39-0.61)</td><td></td><td></td></tr><tr><td>Count</td><td>9</td><td>10</td><td>-</td><td>-</td><td>68</td><td>83</td></tr><tr><td>Estimated abundance</td><td>18<br>(15-23)</td><td>20<br>(16-26)</td><td>-</td><td>-</td><td>136<br>(111-174)</td><td>166<br>(136-213)</td></tr><tr><td rowspan="2">Reproductive season 2012</td><td rowspan="2"></td><td>Survival Rate</td><td>0.47<br>(0.23-0.72)</td><td>0.52<br>(0.28-0.76)</td><td>-</td><td>-</td><td>0.58<br>(0.43-0.72)</td><td>0.63<br>(0.49-0.76)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Fall 2012</td><td rowspan="3">September 29 to October 8 2012</td><td>Detectability</td><td></td><td></td><td colspan="2">0.42 (0.33-0.51)</td><td></td><td></td></tr><tr><td>Count</td><td>2</td><td>5</td><td>40</td><td>67</td><td>70</td><td>70</td></tr><tr><td>Estimated abundance</td><td>5<br>(4-6)</td><td>12<br>(10-15)</td><td>96<br>(79-121)</td><td>161<br>(132-203)</td><td>168<br>(138-213)</td><td>168<br>(138-213)</td></tr><tr><td rowspan="2">Non-reproductive season 2012-2013</td><td rowspan="2"></td><td>Survival Rate</td><td>0.75<br>(0.30-0.96)</td><td>0.79<br>(0.34-0.96)</td><td>0.17<br>(0.08-0.31)</td><td>0.20<br>(0.10-0.35)</td><td>0.75<br>(0.54-0.88)</td><td>0.78<br>(0.59-0.90)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Spring 2013</td><td rowspan="3">March 16 to March 28 2013</td><td>Detectability</td><td></td><td></td><td colspan="2">0.32 (0.25-0.39)</td><td></td><td></td></tr><tr><td>Count</td><td>4</td><td>4</td><td>-</td><td>-</td><td>139</td><td>143</td></tr><tr><td>Estimated abundance</td><td>13<br>(10-16)</td><td>13<br>(10-16)</td><td>-</td><td>-</td><td>441<br>(358-556)</td><td>454<br>(368-572)</td></tr><tr><td rowspan="2">Reproductive season 2013</td><td rowspan="2"></td><td>Survival Rate</td><td>0.91<br>(0.04-1.00)</td><td>0.93<br>(0.05-1.00)</td><td>-</td><td>-</td><td>0.55<br>(0.40-0.70)</td><td>0.60<br>(0.45-0.74)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Fall 2013</td><td rowspan="3">October 2 to October 15 2013</td><td>Detectability</td><td></td><td></td><td colspan="2">0.24 (0.19-0.31)</td><td></td><td></td></tr><tr><td>Count</td><td>0</td><td>5</td><td>224</td><td>232</td><td>134</td><td>110</td></tr><tr><td>Estimated abundance</td><td>-</td><td>21<br>(16-27)</td><td>924<br>(727-1196)</td><td>957<br>(753-1239)</td><td>552<br>(435-715)</td><td>454<br>(357-587)</td></tr></tbody></table>
<table><caption>**Table S1 - Island M1:** State- and sex-specific values extracted from Capture-Mark-Recapture analyses for Island M1. "Detectability" refers to the estimated detection probability (with 95% confidence intervals in brackets) on the island; "Count" refers to the number of individuals (by state and sex) captured during the period of capture ("-" denotes that no individual was found); "Estimated abundance" refers to the estimated number of individuals (by state and sex) on the island (with 95% confidence intervals) and corresponds to "Count" divided by "Detectability" (with "-" indicating the inability to estimate the abundance); "Survival rate" refers to the survival probability (by state and sex) (with 95% confidence intervals in brackets) on the island estimated during the corresponding time lap.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">State</th><th rowspan="2">Sex</th><th colspan="2">Founders</th><th colspan="2">Descendant juveniles</th><th colspan="2">Descendant adults</th></tr><tr><th>Males</th><th>Females</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td rowspan="3">Spring 2011</td><td rowspan="3">April 13 to April 14 2011</td><td>Detectability</td><td></td><td></td><td></td><td>1 (1-1)</td><td></td><td></td><td></td></tr><tr><td>Count</td><td></td><td>30</td><td>15</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Estimated abundance</td><td></td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Reproductive season 2011</td><td rowspan="2"></td><td>Survival Rate</td><td></td><td>0.02 (0.00-0.11)</td><td>0.17 (0.06-0.43)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>0.78 (0.62-0.88)</td><td></td><td></td></tr><tr><td rowspan="3">Fall 2011</td><td rowspan="3">August 10 to August 24 2011</td><td>Detectability</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Count</td><td></td><td>1</td><td>2</td><td>12</td><td>10</td><td>6</td><td>4</td></tr><tr><td>Estimated abundance</td><td></td><td>1 (1-2)</td><td>3 (2-3)</td><td>15 (14-19)</td><td>13 (11-16)</td><td>8 (7-10)</td><td>5 (5-6)</td></tr><tr><td rowspan="2">Non-Reproductive season 2011-2012</td><td rowspan="2"></td><td>Survival Rate</td><td></td><td>0.45 (0.03-0.96)</td><td>0.88 (0.14-1.00)</td><td>0.27 (0.08-0.62)</td><td>0.76 (0.39-0.94)</td><td>0.45 (0.03-0.96)</td><td>0.88 (0.14-1.00)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>0.49 (0.36-0.63)</td><td></td><td></td></tr><tr><td rowspan="3">Spring 2012</td><td rowspan="3">March 28 to April 11 2012</td><td>Detectability</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Count</td><td></td><td>1</td><td>1</td><td>-</td><td>-</td><td>5</td><td>8</td></tr><tr><td>Estimated abundance</td><td></td><td>2 (2-3)</td><td>2 (2-3)</td><td>-</td><td>-</td><td>10 (8-14)</td><td>16 (13-22)</td></tr><tr><td rowspan="2">Reproductive season 2012</td><td rowspan="2"></td><td>Survival Rate</td><td></td><td>-</td><td>0.84 (0.11-1.00)</td><td>-</td><td>-</td><td>0.48 (0.12-0.48)</td><td>0.89 (0.48-0.99)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>0.41 (0.29-0.54)</td><td></td><td></td></tr><tr><td rowspan="3">Fall 2012</td><td rowspan="3">September 29 to October 8 2012</td><td>Detectability</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Count</td><td></td><td>0</td><td>1</td><td>10</td><td>9</td><td>20</td><td>29</td></tr><tr><td>Estimated abundance</td><td></td><td>-</td><td>2 (2-3)</td><td>24 (19-34)</td><td>22 (17-31)</td><td>49 (37-69)</td><td>71 (54-99)</td></tr><tr><td rowspan="2">Non-reproductive season 2012-2013</td><td rowspan="2"></td><td>Survival Rate</td><td></td><td>-</td><td>0.99 (0.48-1.00)</td><td>0.48 (0.10-0.88)</td><td>0.89 (0.43-0.99)</td><td>0.93 (0.29-1.00)</td><td>0.99 (0.70-1.00)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>0.31 (0.22-0.41)</td><td></td><td></td></tr><tr><td rowspan="3">Spring 2013</td><td rowspan="3">March 16 to March 28 2013</td><td>Detectability</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Count</td><td></td><td>0</td><td>1</td><td>-</td><td>-</td><td>16</td><td>29</td></tr><tr><td>Estimated abundance</td><td></td><td>-</td><td>3 (2-4)</td><td>-</td><td>-</td><td>52 (39-71)</td><td>94 (71-130)</td></tr><tr><td rowspan="2">Reproductive season 2013</td><td rowspan="2"></td><td>Survival Rate</td><td></td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.96 (0.02-1.00)</td><td>0.99 (0.11-1.00)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>0.24 (0.17-0.32)</td><td></td><td></td></tr><tr><td rowspan="3">Fall 2013</td><td rowspan="3">October 2 to October 15 2013</td><td>Detectability</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Count</td><td></td><td>0</td><td>0</td><td>34</td><td>37</td><td>66</td><td>66</td></tr><tr><td>Estimated abundance</td><td></td><td>-</td><td>-</td><td>143 (105-202)</td><td>156 (114-220)</td><td>278 (203-392)</td><td>278 (203-392)</td></tr></tbody></table>
<table><caption>**Table S1 - Island M2:** State- and sex-specific values extracted from Capture-Mark-Recapture analyses for Island M2. "Detectability" refers to the estimated detection probability (with 95% confidence intervals in brackets) on the island; "Count" refers to the number of individuals (by state and sex) captured during the period of capture ("-" denotes that no individual was found); "Estimated abundance" refers to the estimated number of individuals (by state and sex) on the island (with 95% confidence intervals) and corresponds to "Count" divided by "Detectability" (with "-" indicating the inability to estimate the abundance); "Survival rate" refers to the survival probability (by state and sex) (with 95% confidence intervals in brackets) on the island estimated during the corresponding time lap.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">State</th><th colspan="2">Founders</th><th colspan="2">Descendant juveniles</th><th colspan="2">Descendant adults</th></tr><tr><th>Sex</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td rowspan="3">Spring 2011</td><td rowspan="3">April 13 to April 14 2011</td><td>Detectability</td><td></td><td></td><td colspan="2">1 (1-1)</td><td></td><td></td></tr><tr><td>Count</td><td>33</td><td>18</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Reproductive season 2011</td><td rowspan="2"></td><td>Survival Rate</td><td>0.02<br>(0.00-0.11)</td><td>0.08<br>(0.02-0.27)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Fall 2011</td><td rowspan="3">August 10 to August 24 2011</td><td>Detectability</td><td></td><td></td><td colspan="2">0.95 (0.70-1.00)</td><td></td><td></td></tr><tr><td>Count</td><td>1</td><td>1</td><td>1</td><td>3</td><td>0</td><td>0</td></tr><tr><td>Estimated abundance</td><td>1<br>(1-1)</td><td>1<br>(1-1)</td><td>1<br>(1-1)</td><td>3<br>(3-4)</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Non-Reproductive season 2011-2012</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>0.08<br>(0.00-0.58)</td><td>0.23<br>(0.02-0.79)</td><td>-</td><td>-</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Spring 2012</td><td rowspan="3">March 28 to April 11 2012</td><td>Detectability</td><td></td><td></td><td colspan="2">0.87 (0.41-0.98)</td><td></td><td></td></tr><tr><td>Count</td><td>0</td><td>0</td><td>-</td><td>-</td><td>3</td><td>4</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>3<br>(3-7)</td><td>5<br>(4-10)</td></tr><tr><td rowspan="2">Reproductive season 2012</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>0.26<br>(0.09-0.54)</td><td>-</td><td>-</td><td>0.46<br>(0.09-0.88)</td><td>0.76<br>(0.30-0.96)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Fall 2012</td><td rowspan="3">September 29 to October 8 2012</td><td>Detectability</td><td></td><td></td><td colspan="2">0.82 (0.34-0.98)</td><td></td><td></td></tr><tr><td>Count</td><td>0</td><td>0</td><td>3</td><td>3</td><td>20</td><td>34</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>4<br>(3-9)</td><td>4<br>(3-9)</td><td>24<br>(20-60)</td><td>41<br>(35-101)</td></tr><tr><td rowspan="2">Non-reproductive season 2012-2013</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>0.02<br>(0.00-0.56)</td><td>0.08<br>(0.00-0.79)</td><td>0.06<br>(0.01-0.23)</td><td>0.18<br>(0.07-0.38)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Spring 2013</td><td rowspan="3">March 16 to March 28 2013</td><td>Detectability</td><td></td><td></td><td colspan="2">0.75 (0.25-0.97)</td><td></td><td></td></tr><tr><td>Count</td><td>0</td><td>0</td><td>-</td><td>-</td><td>3</td><td>11</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>4<br>(3-12)</td><td>15<br>(11-45)</td></tr><tr><td rowspan="2">Reproductive season 2013</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.16<br>(0.02-0.61)</td><td>0.41<br>(0.12-0.77)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Fall 2013</td><td rowspan="3">October 2 to October 15 2013</td><td>Detectability</td><td></td><td></td><td colspan="2">0.68 (0.18-0.95)</td><td></td><td></td></tr><tr><td>Count</td><td>0</td><td>0</td><td>3</td><td>2</td><td>33</td><td>24</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>4<br>(3-16)</td><td>3<br>(2-11)</td><td>49<br>(35-180)</td><td>35<br>(25-131)</td></tr></tbody></table>
<table><caption>**Table S1 - Island M3:** State- and sex-specific values extracted from Capture-Mark-Recapture analyses for Island M3. "Detectability" refers to the estimated detection probability (with 95% confidence intervals in brackets) on the island; "Count" refers to the number of individuals (by state and sex) captured during the period of capture ("-" denotes that no individual was found); "Estimated abundance" refers to the estimated number of individuals (by state and sex) on the island (with 95% confidence intervals) and corresponds to "Count" divided by "Detectability" (with "-" indicating the inability to estimate the abundance); "Survival rate" refers to the survival probability (by state and sex) (with 95% confidence intervals in brackets) on the island estimated during the corresponding time lap.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">State</th><th colspan="2">Founders</th><th colspan="2">Descendant juveniles</th><th colspan="2">Descendant adults</th></tr><tr><th>Males</th><th>Females</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td rowspan="3">Spring 2011</td><td rowspan="3">April 13 to April 14 2011</td><td>Detectability</td><td colspan="8">1 (1-1)</td></tr><tr><td>Count</td><td>70</td><td>36</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Reproductive season 2011</td><td rowspan="2"></td><td>Survival Rate</td><td>0.50<br>(0.37-0.63)</td><td>0.62<br>(0.47-0.75)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Detectability</td><td colspan="8">0.80 (0.66-0.89)</td></tr><tr><td rowspan="3">Fall 2011</td><td rowspan="3">August 10 to August 24 2011</td><td>Count</td><td>24</td><td>24</td><td>50</td><td>38</td><td>7</td><td>15</td></tr><tr><td>Estimated abundance</td><td>30<br>(27-36)</td><td>30<br>(27-36)</td><td>62<br>(56-75)</td><td>47<br>(43-57)</td><td>9<br>(8-11)</td><td>19<br>(17-23)</td></tr><tr><td>Survival Rate</td><td>0.27<br>(0.14-0.47)</td><td>0.38<br>(0.20-0.59)</td><td>0.35<br>(0.21-0.52)</td><td>0.46<br>(0.29-0.64)</td><td>0.32<br>(0.14-0.58)</td><td>0.43<br>(0.20-0.69)</td></tr><tr><td rowspan="3">Spring 2012</td><td rowspan="3">March 28 to April 11 2012</td><td>Detectability</td><td colspan="8">0.53 (0.39-0.67)</td></tr><tr><td>Count</td><td>5</td><td>4</td><td>-</td><td>-</td><td>51</td><td>64</td></tr><tr><td>Estimated abundance</td><td>9<br>(7-13)</td><td>8<br>(6-10)</td><td>-</td><td>-</td><td>96<br>(76-131)</td><td>121<br>(96-164)</td></tr><tr><td rowspan="2">Reproductive season 2012</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>0.26<br>(0.09-0.054)</td><td>-</td><td>-</td><td>0.18<br>(0.10-0.31)</td><td>0.26<br>(0.16-0.41)</td></tr><tr><td>Detectability</td><td colspan="8">0.45 (0.32-0.58)</td></tr><tr><td rowspan="3">Fall 2012</td><td rowspan="3">September 29 to October 8 2012</td><td>Count</td><td>0</td><td>4</td><td>27</td><td>17</td><td>48</td><td>42</td></tr><tr><td>Estimated abundance</td><td>-</td><td>9<br>(7-12)</td><td>61<br>(47-84)</td><td>38<br>(29-53)</td><td>108<br>(83-150)</td><td>94<br>(73-131)</td></tr><tr><td>Survival Rate</td><td>-</td><td>-</td><td>0.16<br>(0.06-0.37)</td><td>0.24<br>(0.09-0.49)</td><td>0.38<br>(0.20-0.59)</td><td>0.50<br>(0.28-0.71)</td></tr><tr><td rowspan="3">Spring 2013</td><td rowspan="3">March 16 to March 28 2013</td><td>Detectability</td><td colspan="8">0.34 (0.24-0.46)</td></tr><tr><td>Count</td><td>0</td><td>0</td><td>-</td><td>-</td><td>38</td><td>38</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>111<br>(82-159)</td><td>111<br>(82-159)</td></tr><tr><td rowspan="2">Reproductive season 2013</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.18<br>(0.09-0.35)</td><td>0.27<br>(0.14-0.46)</td></tr><tr><td>Detectability</td><td colspan="8">0.27 (0.18-0.37)</td></tr><tr><td rowspan="3">Fall 2013</td><td rowspan="3">October 2 to October 15 2013</td><td>Count</td><td>0</td><td>0</td><td>56</td><td>61</td><td>73</td><td>35</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>211<br>(150-310)</td><td>230<br>(164-338)</td><td>275<br>(196-405)</td><td>132<br>(94-194)</td></tr></tbody></table>
<table><caption>**Table S1 - Island M4:** State- and sex-specific values extracted from Capture-Mark-Recapture analyses for Island M4. "Detectability" refers to the estimated detection probability (with 95% confidence intervals in brackets) on the island; "Count" refers to the number of individuals (by state and sex) captured during the period of capture ("-" denotes that no individual was found); "Estimated abundance" refers to the estimated number of individuals (by state and sex) on the island (with 95% confidence intervals) and corresponds to "Count" divided by "Detectability" (with "-" indicating the inability to estimate the abundance); "Survival rate" refers to the survival probability (by state and sex) (with 95% confidence intervals in brackets) on the island estimated during the corresponding time lap.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">State</th><th rowspan="2">Sex</th><th colspan="2">Founders</th><th colspan="2">Descendant juveniles</th><th colspan="2">Descendant adults</th></tr><tr><th>Males</th><th>Females</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td rowspan="3">Spring 2011</td><td rowspan="3">April 13 to April 14 2011</td><td>Detectability</td><td></td><td colspan="6">1 (1-1)</td></tr><tr><td>Count</td><td>82</td><td>38</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Reproductive season 2011</td><td rowspan="2"></td><td>Survival Rate</td><td>0.14 (0.08-0.23)</td><td>0.20 (0.11-0.34)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td colspan="6">0.68 (0.50-0.82)</td></tr><tr><td rowspan="3">Fall 2011</td><td rowspan="3">August 10 to August 24 2011</td><td>Detectability</td><td></td><td colspan="6">0.68 (0.50-0.82)</td></tr><tr><td>Count</td><td>5</td><td>6</td><td>14</td><td>12</td><td>7</td><td>15</td></tr><tr><td>Estimated abundance</td><td>7 (6-10)</td><td>9 (7-12)</td><td>21 (17-28)</td><td>18 (15-24)</td><td>10 (9-14)</td><td>22 (18-30)</td></tr><tr><td rowspan="2">Non-Reproductive season 2011-2012</td><td rowspan="2"></td><td>Survival Rate</td><td>0.56 (0.23-0.85)</td><td>0.67 (0.29-0.91)</td><td>0.56 (0.23-0.85)</td><td>0.67 (0.30-0.90)</td><td>0.21 (0.06-0.56)</td><td>0.30 (0.08-0.67)</td></tr><tr><td></td><td colspan="6">0.37 (0.24-0.53)</td></tr><tr><td rowspan="3">Spring 2012</td><td rowspan="3">March 28 to April 11 2012</td><td>Detectability</td><td></td><td colspan="6">0.37 (0.24-0.53)</td></tr><tr><td>Count</td><td>2</td><td>1</td><td>-</td><td>-</td><td>8</td><td>16</td></tr><tr><td>Estimated abundance</td><td>5 (4-8)</td><td>3 (2-4)</td><td>-</td><td>-</td><td>21 (15-33)</td><td>43 (30-67)</td></tr><tr><td rowspan="2">Reproductive season 2012</td><td rowspan="2"></td><td>Survival Rate</td><td>0.85 (0.37-0.98)</td><td>0.90 (0.48-0.99)</td><td>-</td><td>-</td><td>0.49 (0.18-0.82)</td><td>0.61 (0.27-0.87)</td></tr><tr><td></td><td colspan="6">0.30 (0.19-0.43)</td></tr><tr><td rowspan="3">Fall 2012</td><td rowspan="3">September 29 to October 8 2012</td><td>Detectability</td><td></td><td colspan="6">0.30 (0.19-0.43)</td></tr><tr><td>Count</td><td>3</td><td>2</td><td>19</td><td>16</td><td>20</td><td>16</td></tr><tr><td>Estimated abundance</td><td>10 (7-16)</td><td>7 (5-11)</td><td>64 (44-100)</td><td>54 (37-84)</td><td>67 (46-105)</td><td>54 (37-84)</td></tr><tr><td rowspan="2">Non-reproductive season 2012-2013</td><td rowspan="2"></td><td>Survival Rate</td><td>0.91 (0.38-0.99)</td><td>0.94 (0.47-1.00)</td><td>0.58 (0.23-0.87)</td><td>0.69 (0.30-0.92)</td><td>0.52 (0.19-0.83)</td><td>0.63 (0.25-0.89)</td></tr><tr><td></td><td colspan="6">0.21 (0.14-0.32)</td></tr><tr><td rowspan="3">Spring 2013</td><td rowspan="3">March 16 to March 28 2013</td><td>Detectability</td><td></td><td colspan="6">0.21 (0.14-0.32)</td></tr><tr><td>Count</td><td>0</td><td>1</td><td>-</td><td>-</td><td>23</td><td>22</td></tr><tr><td>Estimated abundance</td><td>-</td><td>5 (3-7)</td><td>-</td><td>-</td><td>107 (71-170)</td><td>102 (68-163)</td></tr><tr><td rowspan="2">Reproductive season 2013</td><td rowspan="2"></td><td>Survival Rate</td><td>0.99 (0.22-1.00)</td><td>0.99 (0.31-1.00)</td><td>-</td><td>-</td><td>0.60 (0.20-0.90)</td><td>0.70 (0.30-0.93)</td></tr><tr><td></td><td colspan="6">0.16 (0.10-0.25)</td></tr><tr><td rowspan="3">Fall 2013</td><td rowspan="3">October 2 to October 15 2013</td><td>Detectability</td><td></td><td colspan="6">0.16 (0.10-0.25)</td></tr><tr><td>Count</td><td>2</td><td>1</td><td>26</td><td>24</td><td>56</td><td>33</td></tr><tr><td>Estimated abundance</td><td>13 (8-20)</td><td>6 (4-10)</td><td>163 (104-266)</td><td>150 (96-145)</td><td>350 (224-572)</td><td>206 (132-337)</td></tr></tbody></table>
<table><caption>**Table S1 - Island M5:** State- and sex-specific values extracted from Capture-Mark-Recapture analyses for Island M5. "Detectability" refers to the estimated detection probability (with 95% confidence intervals in brackets) on the island; "Count" refers to the number of individuals (by state and sex) captured during the period of capture ("-" denotes that no individual was found); "Estimated abundance" refers to the estimated number of individuals (by state and sex) on the island (with 95% confidence intervals) and corresponds to "Count" divided by "Detectability" (with "-" indicating the inability to estimate the abundance); "Survival rate" refers to the survival probability (by state and sex) (with 95% confidence intervals in brackets) on the island estimated during the corresponding time lap.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">State</th><th colspan="2">Founders</th><th colspan="2">Descendant juveniles</th><th colspan="2">Descendant adults</th></tr><tr><th>Sex</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th><th>Males</th><th>Females</th></tr></thead><tbody><tr><td rowspan="3">Spring 2011</td><td rowspan="3">April 13 to April 14 2011</td><td>Detectability</td><td></td><td></td><td colspan="4">1 (1-1)</td></tr><tr><td>Count</td><td>104</td><td>55</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">Reproductive season 2011</td><td rowspan="2"></td><td>Survival Rate</td><td>0.13<br>(0.07-0.22)</td><td>0.19<br>(0.10-0.33)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Detectability</td><td colspan="6">0.70 (0.53-0.83)</td></tr><tr><td rowspan="3">Fall 2011</td><td rowspan="3">August 10 to August 24 2011</td><td>Count</td><td>7</td><td>8</td><td>6</td><td>2</td><td>2</td><td>2</td></tr><tr><td>Estimated abundance</td><td>10<br>(8-13)</td><td>11<br>(10-15)</td><td>9<br>(7-11)</td><td>3<br>(2-4)</td><td>3<br>(2-4)</td><td>3<br>(2-4)</td></tr><tr><td>Non-Reproductive season 2011-2012</td><td>Survival Rate</td><td>-</td><td>0.24<br>(0.07-0.57)</td><td>0.64<br>(0.17-0.94)</td><td>0.74<br>(0.25-0.96)</td><td>0.38<br>(0.07-0.85)</td><td>0.50<br>(0.09-0.91)</td></tr><tr><td rowspan="3">Spring 2012</td><td rowspan="3">March 28 to April 11 2012</td><td>Detectability</td><td></td><td></td><td colspan="4">0.39 (0.27-0.53)</td></tr><tr><td>Count</td><td>0</td><td>1</td><td>-</td><td>-</td><td>12</td><td>3</td></tr><tr><td>Estimated abundance</td><td>-</td><td>3<br>(2-4)</td><td>-</td><td>-</td><td>30<br>(23-44)</td><td>8<br>(6-11)</td></tr><tr><td rowspan="2">Reproductive season 2012</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.63<br>(0.20-0.92)</td><td>0.74<br>(0.28-0.95)</td></tr><tr><td>Detectability</td><td colspan="6">0.32 (0.22-0.43)</td></tr><tr><td rowspan="3">Fall 2012</td><td rowspan="3">September 29 to October 8 2012</td><td>Count</td><td>0</td><td>0</td><td>13</td><td>22</td><td>24</td><td>3</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>69<br>(51-100)</td><td>41<br>(30-59)</td><td>76<br>(55-109)</td><td>9<br>(7-14)</td></tr><tr><td>Non-reproductive season 2012-2013</td><td>Survival Rate</td><td>-</td><td>-</td><td>0.79<br>(0.12-0.99)</td><td>0.86<br>(0.17-0.99)</td><td>0.83<br>(0.23-0.99)</td><td>0.89<br>(0.29-0.99)</td></tr><tr><td rowspan="3">Spring 2013</td><td rowspan="3">March 16 to March 28 2013</td><td>Detectability</td><td></td><td></td><td colspan="4">0.23 (0.16-0.32)</td></tr><tr><td>Count</td><td>0</td><td>0</td><td>-</td><td>-</td><td>41</td><td>23</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>-</td><td>-</td><td>177<br>(128-253)</td><td>99<br>(72-142)</td></tr><tr><td rowspan="2">Reproductive season 2013</td><td rowspan="2"></td><td>Survival Rate</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.57<br>(0.28-0.82)</td><td>0.69<br>(0.38-0.89)</td></tr><tr><td>Detectability</td><td colspan="6">0.17 (0.12-0.24)</td></tr><tr><td rowspan="3">Fall 2013</td><td rowspan="3">October 2 to October 15 2013</td><td>Count</td><td>0</td><td>0</td><td>67</td><td>49</td><td>75</td><td>52</td></tr><tr><td>Estimated abundance</td><td>-</td><td>-</td><td>387<br>(276-556)</td><td>283<br>(202-407)</td><td>433<br>(309-622)</td><td>300<br>(214-431)</td></tr></tbody></table>
Goodness-of-fit test for the dataset

Before running the CMR analysis under the conditional Arnason-Schwarz model, we implemented the dataset in U-Care using Goodness-of-Fit tests for single-state data (Choquet et al. 2009a). We arranged the dataset so that every individual encountered has a value of “1” independent of its state (i.e. “founder”, “descendant juvenile”, “descendant adult”). The global test, with sex- and population-specific groups, was not significant (number of groups =18, df =173, Quadratic Chi² =162.7, p-value=0.702). The test for trap-dependence (potential positive (trap-attractiveness) or negative (trap-shyness) bias in recapturing individuals already captured) was also not significant (N(0,1) signed statistic for trap-dependence =-1.233, p-value, two-sided test =0.217) whereas the test for transience (bias related to individuals that were transient over the surveyed locations and thus were only encountered once during the long-term survey) was significant (N(0,1) statistic for transient (>0) =2.844, p-level, two-sided test =0.004, p-value one-sided test for transience =0.002). Due to geographic isolation of the islands and the low dispersal of brown anoles (decreasing the likelihood of transience) as well as the high mortality rate and short life span of brown anoles (which means that many individuals can be encountered only once in their life), issues with transience within the data are unlikely.
## Model settings in E-Surge

We only set a single class of age, and we ran the model by setting the other parameters as follows: Compute CI hessian; we used EM(20)+Quasi-Newton parameter for non-linear; we set initial values as multiple random (with 15 entries); we used an analytical gradient for the model rank and optimization; we only allowed one cycle, which was sufficient for models to converge (Choquet *et al.* 2009b).

**Table S2-a.** Matrix design for initial state. The initial state model included three two-way interactions: between time and state, time and sex, and population and time. For the introduction event (release of the founders), we fixed frequency for founders to 1 and frequency for descendant juveniles and descendant adults to 0.

<table><thead><tr><th></th><th>First captured as founder</th><th>First captured as descendant juvenile</th><th>First captured as descendant adult</th></tr></thead><tbody><tr><th>Frequency</th><td>a<sub>1</sub></td><td>a<sub>2</sub></td><td>1-a<sub>1</sub>-a<sub>2</sub></td></tr></tbody></table>

**Table S2-b.** Matrix design for state-specific survival probability ($\phi$) between capture event “t” and capture event “t+1”. We designed the survival matrix so descendant juveniles must survive before transitioning to become descendant adults.

<table><thead><tr><th>t</th><th>Alive as founder</th><th>Alive as descendant juvenile</th><th>Alive as descendant adult</th><th>Dead</th></tr></thead><tbody><tr><th>Alive as founder</th><td>Φ<sub>1</sub></td><td>-</td><td>-</td><td>1-Φ<sub>1</sub></td></tr><tr><th>Alive as descendant juvenile</th><td>-</td><td>Φ<sub>2</sub></td><td>-</td><td>1-Φ<sub>2</sub></td></tr><tr><th>Alive as descendant adult</th><td>-</td><td>-</td><td>Φ<sub>3</sub></td><td>1-Φ<sub>3</sub></td></tr><tr><th>Dead</th><td>-</td><td>-</td><td>-</td><td>1</td></tr></tbody></table>

**Table S2-c.** Matrix design for state-state transition probability between capture event “t” and capture event “t+1”. We only allowed transition from descendant juveniles to descendant juveniles or descendant adults. We set values based on preliminary models.

<table><thead><tr><th>t</th><th>Founder</th><th>Descendant juvenile</th><th>Descendant adult</th><th>Dead</th></tr></thead><tbody><tr><th>Founder</th><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Descendant juvenile</th><td>0</td><td>0.15</td><td>0.85</td><td>0</td></tr><tr><th>Descendant adult</th><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><th>Dead</th><td>0</td><td>0</td><td>0</td><td>1</td></tr></tbody></table>
<table><caption>**Table S2-d.** Matrix design for detection probability *p*.</caption><thead><tr><th>Capture event<br>State</th><th>Not captured</th><th>Captured as founder</th><th>Captured as descendant juvenile</th><th>Captured as descendant adult</th></tr></thead><tbody><tr><th>Founder</th><td>1-p</td><td>p</td><td>0</td><td>0</td></tr><tr><th>Descendant juvenile</th><td>1-p</td><td>0</td><td>p</td><td>0</td></tr><tr><th>Descendant adult</th><td>1-p</td><td>0</td><td>0</td><td>p</td></tr><tr><th>Dead</th><td>1</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>
<table><caption>**Table S3:** Model selection to find the best-fit model to estimate survival probability (Φ) and detection probability (p) under the Arnason-Schwarz model. Asterisks represent the additional effect as well as the interaction between two covariates. The model in bold face was the best fit model and was retained for analysis. Models are sorted by ΔQAICc. The first model presented is the one used to estimate survival and detection probabilities. The term “occasion” refers to time when we released (first occasion) or captured individuals; “state” refers to individual age class (founder, descendant juvenile, descendant adult) and time_lapse refers to elapsed time between two occasions of capture.</caption><thead><tr><th>Model</th><th>Number of parameters</th><th>QAICc</th><th>ΔQAICc</th></tr></thead><tbody><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse + population)</td><td>157</td><td>12708.6</td><td>0</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse + sex + population)</td><td>158</td><td>12711</td><td>2.4</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse * sex + population)</td><td>163</td><td>12711</td><td>2.4</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse )</td><td>149</td><td>12717.5</td><td>8.9</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse + sex * population)</td><td>166</td><td>12717.7</td><td>9.1</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse + sex)</td><td>150</td><td>12719.3</td><td>10.7</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse * sex + sex * population)</td><td>171</td><td>12720.7</td><td>12.1</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse * sex)</td><td>155</td><td>12724.1</td><td>15.5</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse * population + sex)</td><td>198</td><td>12733.6</td><td>25</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse * population)</td><td>197</td><td>12737.3</td><td>28.7</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse * population + sex * population)</td><td>206</td><td>12737.6</td><td>29</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse * sex + time_lapse * population)</td><td>203</td><td>12740.9</td><td>32.3</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(time_lapse * sex + time_lapse * population + sex * population)</td><td>211</td><td>12747.7</td><td>39.1</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(population)</td><td>152</td><td>12767.2</td><td>58.6</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(sex + population)</td><td>153</td><td>12768.5</td><td>59.9</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(sex * population)</td><td>161</td><td>12777.8</td><td>69.2</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(.)</td><td>144</td><td>12778.6</td><td>70</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex * population), p(sex)</td><td>145</td><td>12779.6</td><td>71</td></tr><tr><td>Φ(occasion * state + occasion * sex + occasion * population + state * population + sex * population), p(.)</td><td>149</td><td>12781.6</td><td>73</td></tr><tr><td>Φ(occasion * state + occasion * sex + occasion * population + population * sex), p(.)</td><td>133</td><td>12786.4</td><td>77.8</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population + sex), p(.)</td><td>136</td><td>12788.4</td><td>79.8</td></tr><tr><td>Φ(occasion * state + occasion * sex + occasion * population + state * population), p(.)</td><td>141</td><td>12790.8</td><td>82.2</td></tr><tr><td>Φ(occasion * state + occasion * population + sex), p(.)</td><td>120</td><td>12793.9</td><td>85.3</td></tr><tr><td>Φ(occasion * population + state * population + sex * population), p(.)</td><td>136</td><td>12801.8</td><td>93.2</td></tr><tr><td>Φ(occasion * sex + occasion * population + state * population + population * sex), p(.)</td><td>141</td><td>12805.8</td><td>97.2</td></tr><tr><td>Φ(occasion * population + state * population + sex), p(.)</td><td>128</td><td>12813.8</td><td>105.2</td></tr><tr><td>Φ(occasion * population + population * sex + state), p(.)</td><td>120</td><td>12825.3</td><td>116.7</td></tr><tr><td>Φ(occasion * state + occasion * population + state * population), p(.)</td><td>135</td><td>12831.7</td><td>123.1</td></tr><tr><td>Φ(occasion * state + occasion * population), p(.)</td><td>119</td><td>12835.9</td><td>127.3</td></tr><tr><td>Φ(occasion * population + state + sex), p(.)</td><td>112</td><td>12837.2</td><td>128.6</td></tr><tr><td>Φ(occasion * sex + occasion * population + state), p(.)</td><td>117</td><td>12840.2</td><td>131.6</td></tr><tr><td>Φ(occasion * state + occasion * sex + occasion * population + state * sex + state * population + population * sex), p(.)</td><td>239</td><td>12845.7</td><td>137.1</td></tr></tbody></table>
<table><thead><tr><th>Model</th><th>Number of parameters</th><th>QAICc</th><th>ΔAICc</th></tr></thead><tbody><tr><td>Φ(occasion * population + state * population), p(.)</td><td>127</td><td>12855.9</td><td>147.3</td></tr><tr><td>Φ(occasion * population + state), p(.)</td><td>111</td><td>12877.5</td><td>168.9</td></tr><tr><td>Φ(occasion * population + population * sex), p(.)</td><td>118</td><td>12902.4</td><td>193.8</td></tr><tr><td>Φ(occasion * sex + occasion * population + population * sex), p(.)</td><td>123</td><td>12905.4</td><td>196.8</td></tr><tr><td>Φ(occasion * population + sex), p(.)</td><td>110</td><td>12916.5</td><td>207.9</td></tr><tr><td>Φ(occasion * sex + occasion * population), p(.)</td><td>115</td><td>12918.4</td><td>209.8</td></tr><tr><td>Φ(occasion * population), p(.)</td><td>109</td><td>12956.4</td><td>247.8</td></tr><tr><td>Φ(occasion * state + state * population + sex * population), p(.)</td><td>104</td><td>12963.1</td><td>254.5</td></tr><tr><td>Φ(occasion * state + occasion * sex + state * population), p(.)</td><td>101</td><td>12965.1</td><td>256.5</td></tr><tr><td>Φ(occasion * sex + state * sex + state * population), p(.)</td><td>101</td><td>12980.5</td><td>271.9</td></tr><tr><td>Φ(occasion + state * population + sex), p(.)</td><td>88</td><td>12992.4</td><td>283.8</td></tr><tr><td>Φ(occasion * state + state * population), p(.)</td><td>95</td><td>13011.3</td><td>302.7</td></tr><tr><td>Φ(occasion + state * population), p(.)</td><td>87</td><td>13034.1</td><td>325.5</td></tr><tr><td>Φ(state * population + sex * population), p(.)</td><td>91</td><td>13036.7</td><td>328.1</td></tr><tr><td>Φ(state * sex + state * population + sex * population), p(.)</td><td>93</td><td>13040.8</td><td>332.2</td></tr><tr><td>Φ(state * population + sex), p(.)</td><td>83</td><td>13042.7</td><td>334.1</td></tr><tr><td>Φ(occasion * state + occasion * sex + state * population), p(.)</td><td>93</td><td>13043.7</td><td>335.1</td></tr><tr><td>Φ(state * sex + state * population), p(.)</td><td>85</td><td>13046</td><td>337.4</td></tr><tr><td>Φ(occasion * state + sex + population), p(.)</td><td>80</td><td>13058.1</td><td>349.5</td></tr><tr><td>Φ(occasion + state + sex * population), p(.)</td><td>80</td><td>13084.9</td><td>376.3</td></tr><tr><td>Φ(state * population), p(.)</td><td>82</td><td>13087.3</td><td>378.7</td></tr><tr><td>Φ(occasion * sex + population + state), p(.)</td><td>77</td><td>13087.4</td><td>378.8</td></tr><tr><td>Φ(occasion + state + sex + population), p(.)</td><td>72</td><td>13091.2</td><td>382.6</td></tr><tr><td>Φ(occasion * state + population), p(.)</td><td>79</td><td>13101.8</td><td>393.2</td></tr><tr><td>Φ(occasion + state + population), p(.)</td><td>71</td><td>13132.6</td><td>424</td></tr><tr><td>Φ(occasion * sex + population * sex), p(.)</td><td>83</td><td>13133.5</td><td>424.9</td></tr><tr><td>Φ(state + population * sex), p(.)</td><td>75</td><td>13140</td><td>431.4</td></tr><tr><td>Φ(occasion + sex * population), p(.)</td><td>78</td><td>13140</td><td>431.4</td></tr><tr><td>Φ(state * sex + sex * population), p(.)</td><td>77</td><td>13144</td><td>435.4</td></tr><tr><td>Φ(state + sex + population), p(.)</td><td>67</td><td>13147.5</td><td>438.9</td></tr><tr><td>Φ(occasion + sex + population), p(.)</td><td>70</td><td>13147.7</td><td>439.1</td></tr><tr><td>Φ(state * sex + population), p(.)</td><td>69</td><td>13151.4</td><td>442.8</td></tr><tr><td>Φ(occasion + population), p(.)</td><td>69</td><td>13190.4</td><td>481.8</td></tr><tr><td>Φ(state + population), p(.)</td><td>66</td><td>13192</td><td>483.4</td></tr><tr><td>Φ(occasion * state), p(.)</td><td>71</td><td>13196.5</td><td>487.9</td></tr><tr><td>Φ(sex * population), p(.)</td><td>73</td><td>13197.5</td><td>488.9</td></tr><tr><td>Φ(sex + population), p(.)</td><td>65</td><td>13207.3</td><td>498.7</td></tr><tr><td>Φ(state + sex), p(.)</td><td>59</td><td>13220.6</td><td>512</td></tr><tr><td>Φ(occasion + state), p(.)</td><td>63</td><td>13221</td><td>512.4</td></tr><tr><td>Φ(state * sex), p(.)</td><td>61</td><td>13224.6</td><td>516</td></tr><tr><td>Φ(occasion * sex), p(.)</td><td>67</td><td>13229.9</td><td>521.3</td></tr><tr><td>Φ(occasion + sex), p(.)</td><td>62</td><td>13231</td><td>522.4</td></tr></tbody></table>
<table><thead><tr><td>Model</td><td>Number of parameters</td><td>QAICc</td><td>ΔQAICc</td></tr></thead><tbody><tr><td>Φ(state), p(.)</td><td>58</td><td>13262.6</td><td>554</td></tr><tr><td>Φ(state), p(.)</td><td>58</td><td>13262.6</td><td>554</td></tr><tr><td>Φ(occasion), p(.)</td><td>61</td><td>13272</td><td>563.4</td></tr><tr><td>Φ(sex), p(.)</td><td>57</td><td>13284.7</td><td>576.1</td></tr><tr><td>Φ(occasion * sex + population), p(.)</td><td>84</td><td>14480.4</td><td>1771.8</td></tr></tbody></table>
<table><caption>**Table S4:** Estimated values for a) sex ratio, b) offspring production and c) population annual growth on each island. Sex ratio was calculated as the number of adult males (founders and descendants) estimated at the beginning of each breeding season divided by the total number of adults (founders and descendants, males and females) estimated at the beginning of the same breeding season. Viable offspring production was calculated as the number of juveniles (males and females) estimated at the end of each breeding season divided by the number of adult females (founders and descendants) estimated at the beginning of that breeding season. Population annual growth was calculated as the difference between the number of adults (males and females) estimated at the beginning of two following reproductive seasons (t and t+1) divided by the number of adults (males and females) estimated at the beginning of reproductive season t.</caption><thead><tr><th rowspan="2">Island</th><th colspan="3">a) Sex ratio</th><th colspan="3">b) Offspring production</th><th colspan="2">c) Population annual growth</th></tr><tr><th>2011</th><th>2012</th><th>2013</th><th>2011</th><th>2012</th><th>2013</th><th>2011-2012</th><th>2012-2013</th></tr></thead><tbody><tr><td>F1</td><td>0.30</td><td>0.36</td><td>0.49</td><td>1.54</td><td>2.02</td><td>1.33</td><td>0.81</td><td>2.00</td></tr><tr><td>F2</td><td>0.34</td><td>0.26</td><td>0.45</td><td>3.77</td><td>0.84</td><td>2.72</td><td>0.23</td><td>0.57</td></tr><tr><td>F3</td><td>0.32</td><td>0.41</td><td>0.38</td><td>1.72</td><td>1.08</td><td>7.83</td><td>1.16</td><td>0.60</td></tr><tr><td>F4</td><td>0.33</td><td>0.45</td><td>0.49</td><td>0.81</td><td>1.38</td><td>4.03</td><td>1.81</td><td>1.71</td></tr><tr><td>M1</td><td>0.67</td><td>0.40</td><td>0.35</td><td>1.87</td><td>2.57</td><td>3.08</td><td>-0.33</td><td>2.31</td></tr><tr><td>M2</td><td>0.65</td><td>0.38</td><td>0.21</td><td>0.22</td><td>1.60</td><td>0.47</td><td>-0.84</td><td>1.38</td></tr><tr><td>M3</td><td>0.66</td><td>0.45</td><td>0.50</td><td>3.03</td><td>0.77</td><td>3.97</td><td>1.21</td><td>-0.05</td></tr><tr><td>M4</td><td>0.68</td><td>0.36</td><td>0.50</td><td>1.02</td><td>2.57</td><td>2.93</td><td>-0.40</td><td>1.97</td></tr><tr><td>M5</td><td>0.65</td><td>0.73</td><td>0.64</td><td>0.22</td><td>10.00</td><td>6.77</td><td>-0.74</td><td>5.73</td></tr></tbody></table>
<table><caption>**Table S5.** Outputs from the linear mixed model with offspring production as the dependent variable, and propagule size and year as independent variables. The model also included the two-way interaction between propagule size and year. Population was included as a random effect to account for repeated measures. Table a shows results with all data included, and Table b shows results with the outlier removed (see figure 3a for the outlier)</caption><thead><tr><th colspan="4">a) Results with all data included</th></tr><tr><th></th><th>Estimates (sd)</th><th>t-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>1.578 (0.644)</td><td>2.542</td><td>0.014</td></tr><tr><td><strong>Propagule size (standardized) Wald χ<sup>2</sup><sub>1</sub>=6.3617 (p = 0.0117)</strong></td><td>-0.298 (0.656)</td><td>-0.454</td><td>0.650</td></tr><tr><td><strong>Year Wald χ<sup>2</sup><sub>2</sub>=5.3559 (p = 0.0687)</strong></td><td>-</td><td>-</td><td>-</td></tr><tr><td>2011</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2012</td><td>0.958 (0.910)</td><td>1.052</td><td>0.293</td></tr><tr><td>2013</td><td>2.103 (0.910)</td><td>2.311</td><td>0.021</td></tr><tr><td><strong>Propagule size x Year Wald χ<sup>2</sup><sub>2</sub>=5.5244 (p = 0.0632)</strong></td><td>-</td><td>-</td><td>-</td></tr><tr><td>Propagule size x 2011</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Propagule size x 2012</td><td>1.773 (0.943)</td><td>1.912</td><td>0.056</td></tr><tr><td>Propagule size x 2013</td><td>1.984 (0.927)</td><td>2.140</td><td>0.032</td></tr></tbody></table>

<table><thead><tr><th colspan="4">b) Results with outlier removed</th></tr><tr><th></th><th>Estimates (sd)</th><th>t-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>1.578 (0.435)</td><td>3.624</td><td>0.0003</td></tr><tr><td><strong>Propagule size (standardized) Wald χ<sup>2</sup><sub>1</sub>=6.3617 (p = 0.0117)</strong></td><td>-0.298 (0.444)</td><td>-0.671</td><td>0.502</td></tr><tr><td><strong>Year Wald χ<sup>2</sup><sub>2</sub>=5.3559 (p = 0.0687)</strong></td><td>-</td><td>-</td><td>-</td></tr><tr><td>2011</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2012</td><td>0.027 (0.645)</td><td>-0.042</td><td>0.967</td></tr><tr><td>2013</td><td>2.103 (0.616)</td><td>3.416</td><td>0.0006</td></tr><tr><td><strong>Propagule size x Year Wald χ<sup>2</sup><sub>2</sub>=5.5244 (p = 0.0632)</strong></td><td>-</td><td>-</td><td>-</td></tr><tr><td>Propagule size x 2011</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Propagule size x 2012</td><td>0.050 (0.713)</td><td>0.071</td><td>0.944</td></tr><tr><td>Propagule size x 2013</td><td>1.984 (0.627)</td><td>3.163</td><td>0.0016</td></tr></tbody></table>
<table><caption>**Table S6.** Outputs from the linear mixed model with population annual growth as the dependent variable, and propagule size and year as independent variables. The model also included a two-way interaction between propagule size and year. Population was included as a random effect to account for repeated measures. Table a shows results with all data included, and Table b shows results with the outlier removed (see figure 3a for the outlier)</caption><caption>a) Results with all data included</caption><thead><tr><th></th><th>Estimates (sd)</th><th>t-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.323 (0.455)</td><td>0.711</td><td>0.477</td></tr><tr><td><strong>Propagule size (standardized) Wald χ<sup>2</sup><sub>1</sub>=1.2586 (p = 0.2619)</strong></td><td>0.097 (0.468)</td><td>0.207</td><td>0.836</td></tr><tr><td><strong>Year Wald χ<sup>2</sup><sub>1</sub>=5.2826 (p = 0.0215)</strong></td><td></td><td></td><td></td></tr><tr><td>2011 - 2012</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2012 - 2013</td><td>1.479 (0.643)</td><td>2.298</td><td>0.0215</td></tr><tr><td><strong>Propagule size x Year Wald χ<sup>2</sup><sub>1</sub>=0.6879 (p = 0.4069)</strong></td><td></td><td></td><td></td></tr><tr><td>Propagule size x 2011 - 2012</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Propagule size x 2012 - 2013</td><td>0.549 (0.662)</td><td>0.829</td><td>0.407</td></tr></tbody></table>

<table><caption>b) Results with outlier removed</caption><thead><tr><th></th><th>Estimates (sd)</th><th>t-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.323 (0.315)</td><td>1.026</td><td>0.305</td></tr><tr><td><strong>Propagule size (standardized) Wald χ<sup>2</sup><sub>1</sub>=1.2586 (p = 0.2619)</strong></td><td>0.097 (0.324)</td><td>0.299</td><td>0.765</td></tr><tr><td><strong>Year Wald χ<sup>2</sup><sub>1</sub>=5.2826 (p = 0.0215)</strong></td><td></td><td></td><td></td></tr><tr><td>2011 - 2012</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2012 - 2013</td><td>0.915 (0.467)</td><td>1.959</td><td>0.050</td></tr><tr><td><strong>Propagule size x Year Wald χ<sup>2</sup><sub>1</sub>=0.6879 (p = 0.4069)</strong></td><td></td><td></td><td></td></tr><tr><td>Propagule size x 2011 - 2012</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Propagule size x 2012 - 2013</td><td>-0.447 (0.521)</td><td>-0.857</td><td>0.391</td></tr></tbody></table>
<table><caption>**Table S7.** Outputs from the linear mixed model with offspring production as the dependent variable, and propagule size (as the number of introduced females) and year as independent variables. The model also included the two-way interaction between propagule size and year. Population was included as a random effect to account for repeated measures.</caption><thead><tr><th></th><th>Estimates (sd)</th><th>t-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>1.578 (0.725)</td><td>2.175</td><td>0.030</td></tr><tr><td>Propagule size (standardized) Wald χ<sup>2</sup><sub>1</sub>=1.6824 (p = 0.1946)</td><td>-0.056 (0.739)</td><td>-0.076</td><td>0.940</td></tr><tr><td>Year Wald χ<sup>2</sup><sub>2</sub>=4.4376 (p = 0.1087)</td><td></td><td></td><td></td></tr><tr><td>2011</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2012</td><td>0.958 (1.000)</td><td>0.958</td><td>0.338</td></tr><tr><td>2013</td><td>2.103 (1.000)</td><td>2.104</td><td>0.035</td></tr><tr><td>Propagule size x Year Wald χ<sup>2</sup><sub>2</sub>=3.1893 (p = 0.2030)</td><td></td><td></td><td></td></tr><tr><td>Propagule size x 2011</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Propagule size x 2012</td><td>0.231 (1.019)</td><td>0.227</td><td>0.820</td></tr><tr><td>Propagule size x 2013</td><td>1.679 (1.019)</td><td>1.648</td><td>0.099</td></tr></tbody></table>
<table><caption>**Table S8.** Outputs from the linear mixed model with population annual growth as the dependent variable, and propagule size (as the number of introduced females) and year as independent variables. The model also included the two-way interaction between propagule size and year. Population was included as a random effect to account for repeated measures.</caption><thead><tr><th></th><th>Estimates (sd)</th><th>t-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.323 (0.464)</td><td>0.697</td><td>0.486</td></tr><tr><td><strong>Propagule size (standardized) Wald χ<sup>2</sup><sub>1</sub>=0.7170 (p = 0.3971)</strong></td><td>0.551 (0.477)</td><td>1.153</td><td>0.249</td></tr><tr><td><strong>Year Wald χ<sup>2</sup><sub>1</sub>=5.0790 (p = 0.0242)</strong></td><td></td><td></td><td></td></tr><tr><td>2011 - 2012</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2012 - 2013</td><td>1.479 (0.656)</td><td>2.254</td><td>0.024</td></tr><tr><td><strong>Propagule size x Year Wald χ<sup>2</sup><sub>1</sub>=0.6147 (p = 0.4330)</strong></td><td></td><td></td><td></td></tr><tr><td>Propagule size x 2011 - 2012</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Propagule size x 2012 - 2013</td><td>-0.529 (0.675)</td><td>-0.784</td><td>0.433</td></tr></tbody></table>
<table><caption>**Table S9.** Outputs from a generalized linear mixed model testing the effect of state, sex and sex ratio on survival rate in Spring 2011 and Winter 2011-2012. The model also included the two-way interactions between state and sex ratio, and sex and sex ratio. Population was also included as a random effect.</caption><thead><tr><th></th><th>Estimates (sd)</th><th>z-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>-0.679 (1.068)</td><td>-0.636</td><td>0.525</td></tr><tr><td><strong>State Wald χ<sup>2</sup><sub>2</sub>=1.0456 (p = 0.5929)</strong></td><td></td><td></td><td></td></tr><tr><td>Adult</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Founder</td><td>-0.464 (1.172)</td><td>-0.396</td><td>0.692</td></tr><tr><td>Juvenile</td><td>0.0004 (1.304)</td><td>0</td><td>1</td></tr><tr><td><strong>Sex Wald χ<sup>2</sup><sub>1</sub>=1.6002 (p = 0.2059)</strong></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Male</td><td>-1.387 (1.056)</td><td>-1.313</td><td>0.189</td></tr><tr><td><strong>Sex ratio Wald χ<sup>2</sup><sub>1</sub>=0.4306 (p = 0.5117)</strong></td><td></td><td></td><td></td></tr><tr><td>Female-biased propagule</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Male-biased propagule</td><td>-0.683 (1.428)</td><td>-0.478</td><td>0.633</td></tr><tr><td><strong>State x Sex ratio Wald χ<sup>2</sup><sub>2</sub>=0.6832 (p = 0.7106)</strong></td><td></td><td></td><td></td></tr><tr><td>Adult x Female-biased propagule</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Adult x Male-biased propagule</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Founder x Female-biased propagule</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Founder x Male-biased propagule</td><td>1.111 (1.512)</td><td>0.735</td><td>0.462</td></tr><tr><td>Juvenile x Female-biased propagule</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Juvenile x Male-biased propagule</td><td>1.245 (1.661)</td><td>0.750</td><td>0.454</td></tr><tr><td><strong>Sex x Sex ratio Wald χ<sup>2</sup><sub>1</sub>=0.5576 (p = 0.4552)</strong></td><td></td><td></td><td></td></tr><tr><td>Female x Female-biased propagule</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Male x Female-biased propagule</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Female x Male-biased propagule</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Male x Male-biased propagule</td><td>0.933 (1.250)</td><td>0.747</td><td>0.455</td></tr></tbody></table>
<table><caption>**Table S10.** Outputs from a generalized linear mixed model testing the effect of state and sex on survival rate, as well as the interaction between state and sex, and population as a random effect.</caption><thead><tr><th></th><th>Estimates (sd)</th><th>z-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.224 (0.538)</td><td>0.417</td><td>0.676</td></tr><tr><td><strong>State Wald χ<sup>2</sup><sub>2</sub>=9.1955 (p = 0.0101)</strong></td><td></td><td></td><td></td></tr><tr><td>Adult</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Founder</td><td>-1.043 (0.515)</td><td>-2.026</td><td>0.043</td></tr><tr><td>Juvenile</td><td>-0.294 (0.636)</td><td>-0.463</td><td>0.644</td></tr><tr><td><strong>Sex Wald χ<sup>2</sup><sub>1</sub>=6.7514 (p = 0.0094)</strong></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Male</td><td>-0.696 (0.533)</td><td>-1.307</td><td>0.191</td></tr><tr><td><strong>State x Sex Wald χ<sup>2</sup><sub>2</sub>=0.3255 (p = 0.850)</strong></td><td></td><td></td><td></td></tr><tr><td>Adult x Female</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Adult x Male</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Founder x Female</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Founder x Male</td><td>-0.291 (0.756)</td><td>-0.385</td><td>0.700</td></tr><tr><td>Juvenile x Female</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Juvenile x Male</td><td>-0.511 (0.949)</td><td>-0.538</td><td>0.590</td></tr></tbody></table>
<table><caption>**Table S11.** Overall survival probabilities among states and sexes for each population (derived from General Linear Mixed Model).</caption><thead><tr><th rowspan="2"></th><th colspan="2">Founder</th><th colspan="2">Adult</th><th colspan="2">Juvenile</th></tr><tr><th>Female</th><th>Male</th><th>Female</th><th>Male</th><th>Female</th><th>Male</th></tr></thead><tbody><tr><td>F1</td><td>0.187</td><td>0.085</td><td>0.427</td><td>0.231</td><td>0.306</td><td>0.151</td></tr><tr><td>F2</td><td>0.178</td><td>0.080</td><td>0.412</td><td>0.220</td><td>0.292</td><td>0.143</td></tr><tr><td>F3</td><td>0.264</td><td>0.126</td><td>0.538</td><td>0.319</td><td>0.407</td><td>0.217</td></tr><tr><td>F4</td><td>0.567</td><td>0.346</td><td>0.810</td><td>0.632</td><td>0.715</td><td>0.503</td></tr><tr><td>M1</td><td>0.520</td><td>0.304</td><td>0.779</td><td>0.587</td><td>0.675</td><td>0.456</td></tr><tr><td>M2</td><td>0.139</td><td>0.061</td><td>0.343</td><td>0.174</td><td>0.236</td><td>0.111</td></tr><tr><td>M3</td><td>0.231</td><td>0.108</td><td>0.493</td><td>0.282</td><td>0.365</td><td>0.188</td></tr><tr><td>M4</td><td>0.572</td><td>0.350</td><td>0.812</td><td>0.636</td><td>0.719</td><td>0.508</td></tr><tr><td>M5</td><td>0.358</td><td>0.184</td><td>0.644</td><td>0.422</td><td>0.516</td><td>0.301</td></tr></tbody><tfoot><tr><td>Mean (sd)</td><td>0.335 (0.175)</td><td>0.183 (0.119)</td><td>0.584 (0.183)</td><td>0.389 (0.186)</td><td>0.470 (0.192)</td><td>0.286 (0.162)</td></tr></tfoot></table>
<figure><img src="image_9.png" /></figure>

**Figure S1.** Estimated abundance across islands and time for males and females. Estimated abundance is calculated for each sex (male, female) and state (founder, descendant juvenile, descendant adult) based on the number of individuals captured during a capture event divided by the detection probability for the corresponding capture event. The x-axis refers to capture events (Spring for March/April, Fall for August to October) from 2011-2013. Islands are ordered from small to large propagule size in the same order as in Table 1. The figure illustrates the establishment of each population except for the population on island M2 which was on the verge of collapse.
<figure><img src="image_10.png" /><figcaption>**Figure S2:** Detection probability on the 9 islands for each capture event. Bars indicate 95% confidence intervals.</figcaption></figure>

**References:**

Choquet, R., Lebreton, J.-D., Gimenez, O., Reboulet, A.-M. & Pradel, R. (2009a). U-CARE: Utilities for performing goodness of fit tests and manipulating CApture-REcapture data. *Ecography*, 32, 1071–1074.

Choquet, R., Rouan, L. & Pradel, R. (2009b). *Program E-SURGE: A Software Application for Fitting Multievent Models*. Springer, New York.