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# The impact of severe drought on survival, fecundity, and population persistence in an endangered amphibian.

Hugo Cayuela, D. Arsovski, E. Bonnaire, R. Duguet, Pierre Joly, A. Besnard

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## To cite this version:

Hugo Cayuela, D. Arsovski, E. Bonnaire, R. Duguet, Pierre Joly, et al.. The impact of severe drought on survival, fecundity, and population persistence in an endangered amphibian.. Ecosphere, 2016, 7 (2 - e01246), pp.1-12. 10.1002/ecs2.1246. hal-01284481

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# The impact of severe drought on survival, fecundity, and population persistence in an endangered amphibian

H. CAYUELA,¹,²,† D. ARSOVSKI,² E. BONNAIRE,³ R. DUGUET,⁴ P. JOLY,¹ AND A. BESNARD²

¹UMR 5023 LEHNA, Laboratoire d'Ecologie des Hydrosystèmes Naturels et Anthropisés, CNRS, Université Lyon 1, 69100 Villeurbanne, France

²CEFE UMR 5175, CNRS – Université de Montpellier – Université Paul-Valéry Montpellier – EPHE, Laboratoire Biogéographie et Ecologie des Vertébrés, 1919 Route de Mende, 34293 Montpellier Cedex 5, France

³Office National des Forêts, Agence de Verdun, 55100, Verdun, France

⁴Alcedo Faune Flore, Quartier Le Fez, 07110, Sanilhac, France

**Citation:** Cayuela, H., D. Arsovski, E. Bonnaire, R. Duguet, P. Joly, and A. Besnard. 2016. The impact of severe drought on survival, fecundity and population persistence in an endangered amphibian. Ecosphere 7(2):e01246.
10.1002/ecs2.1246

**Abstract.** Climate is globally changing. In Europe, studies have highlighted an increasing trend in both the frequency and magnitude of droughts. Abrupt changes in the frequency, location, or intensity of extreme heatwaves and droughts can have direct and severe effects on wild populations. Amphibians are the planet's most threatened group of vertebrates, with over 40% of known species considered in decline. To date, researchers have mainly focused on the influence of repeated droughts on species occurrence and community composition; however, evidence of the direct impact of climatic factors on the demographic parameters of amphibians is currently not well documented. Further investigation of this issue is therefore of critical importance in order to optimize local and global wildlife conservation policies in the context of a changing climate. This study used capture–recapture data to investigate the impact of severe drought on the survival and fecundity of a threatened amphibian, the yellow-bellied toad (*Bombina variegata*, L.), as well as to predict how potential changes in the frequency of droughts might influence the population growth rate. By developing multievent capture-recapture models, we showed that severe drought has a negative impact on fecundity and postmetamorphic survival at different ontogenetic stages. Then, using stochastic matrix population models, we predicted that changes in drought frequency negatively influence the population growth rate, which is a warning sign for population persistence. Direct conservation actions are then proposed to mitigate the detrimental effects of drought on population dynamics.

**Key words:** amphibian; *Bombina variegata*; drought; multievent capture–recapture models.

**Received** 22 April 2015; revised 26 June 2015; accepted 29 June 2015. Corresponding Editor: D. P. C. Peters.

**Copyright:** © 2016 Cayuela et al. This is an open access article under the terms of the Creative Commons Attribution
License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

† **E-mail:** hugo.cayuela@univ-lyon1.fr

## INTRODUCTION

Extreme climate events have been recorded to increase during the last century (McCarthyn 2001). In Europe, records indicate that the frequency and magnitude of heatwaves and droughts have increased in Europe over the last 45 yr, especially in the southern and western

regions (Fink et al. 2004, Vautard et al. 2007). In combination with other environmental factors (e.g., habitat loss, disease, and biological invasion), abrupt changes in the frequency, location, or intensity of these climatic events can have direct and severe effects on wild populations (Parmesan et al. 2000). Understanding how such climatic events may influence population
persistence is therefore a critical issue in conser-
vation biology.

Amphibians are the planet's most threatened
group of vertebrates, with over 40% of known
species considered in decline (Stuart et al. 2004).
Many factors contribute to explain this glob-
al decline (reviewed by Houlahan et al. 2000,
Kiesecker et al. 2001), including habitat loss and
landscape fragmentation (Cushman 2006, Becker
et al. 2007), disease (Voyles 2009), and biological
invasion (Kats and Ferrer 2003). Furthermore,
climate change is expected to lead to changes in
population dynamics (Griffiths et al. 2010, Lowe
2012), species distribution (Scheele et al. 2012),
and community composition (McMenamin et al.
2008). To date, researchers have mainly focused
on the two latter areas; the direct impact of cli-
matic factors on demographic parameters and, in
turn, population viability remains poorly docu-
mented. Of the few studies considering the effect
of climate change on amphibian population dy-
namics, low annual survival rates at adult stage
have been reported due to mild winters during
hibernation (Reading 2007 on *Bufo bufo*, Griffiths
et al. 2010 on *Triturus cristatus*), due to heavy
rainfall during breeding activity (Griffiths et al.
2010 on *T. cristatus*), and due to drought during
breeding activity (Church et al. 2007 on *Ambysto-
ma tigrinum*). A drop in recruitment has also been
sometimes linked to drought (Richter et al. 2003
on *Rana sevosa*, Taylor et al. 2006 on *Ambystoma
opacum*) or to heavy rainfall leading to increased
flood severity (Lowe 2012 on *Gyrinophilus porphy-
riticus*).

However, the consequences of climatic effects
on the population persistence of amphibians
are unfortunately often based on supposition,
and few studies have attempted to predict how
potential changes in climatic conditions might
affect population viability (Church et al. 2007).
Investigating the influence of extreme climate
events such as droughts is therefore of critical
importance in order to optimize local and global
wildlife conservation policies in the context of a
changing climate (Shoo 2011).

In this study, we examined how severe
drought impacts the survival and fecundity of a
small, long-lived anuran of Western and Central
Europe, the yellow-bellied toad (Bombina varie-
gata), as well as the impact of potential changes
in drought frequency on its population growth

rate. The recent extinction of this endangered
amphibian in Belgium and Luxembourg, as well
as in several regions of southern and western
France, is alarming and supports the idea of a
widespread decline (Massemin and Cheylan
2001, Lescure et al. 2011), although the proximate
causes (i.e., habitat loss and fragmentation or dis-
ease) often remain poorly documented (Warren
and Büttner 2008, Canessa et al. 2013, Cayuela
et al. 2015, Scheele et al. 2015).

Using capture–recapture (CR) data collect-
ed on a population of *B. variegata* in southern
France, we investigated whether or not drought
had a negative impact on survival at three on-
togenetic stages (juvenile, subadult, and adult).
It is known that severe dehydration usually has
detrimental effects on many amphibian physi-
ological systems (Hillman 2009). As a deficit of
rainfall may lead to high rates of larval mortality
due to pond desiccation or density-dependent
factors resulting from a reduction in oviposi-
tion site availability (Berven 1990, Walls et al.
2013), we also tested whether or not drought has
a detrimental influence on fecundity. Then us-
ing a stochastic matrix population model (Cas-
well 2001), we examined how future changes in
drought frequency might influence population
persistence. We predicted that a combined re-
duction in both recruitment and survival after
metamorphosis would lead to a negative rela-
tionship between the population growth rate
and drought frequency. We then discussed pos-
sible conservation management perspectives for
the yellow-bellied toad.

METHODS

*Study area and field sampling*

The study was conducted on a population
of *B. variegata* located in the Ardèche de-
partment, southeastern France. The popula-
tion is located along a 2.5-km segment of
the Bourge River. In the study area, the
species breeds in rocky pools and residual
puddles in close vicinity to the river channel
and hibernates in the surrounding woodland
(Cayuela et al. 2011, 2013). The breeding
season extends from late April to late June,
and tadpole metamorphosis occurs between
June and early September. The study took
place over a 5-yr period between 2010 and
<figure><img src="image_4.png" /><figcaption>Fig. 1. Variation in precipitation levels during the active period (April to September) of *Bombina variegata* in a population in southern France between 2010 and 2014: in blue, “standard” years; in orange, drought.</figcaption></figure>

2014. In France, the spring of 2011 was characterized by extremely low precipitation; the average amount of precipitation across the country was approximately 45% of the average recorded from 1971–2000. As such, it was the driest spring in the last 50 yr: drier than that of 1976 (54% of the average amount) or of 1997 (60%). During the study period in the surveyed area (see Fig. 1), monthly precipitation varied from 4.5 mm (April 2011) to 161.1 mm (September 2014), with 2011 being the driest year (a cumulative rainfall of 38.1 mm between April and September), and 2014 the wettest (438 mm between April and September).

The toads were surveyed using the CR method. Toads were caught by hand or with a dip net during daylight hours (09:00–19:00) during the breeding season, from late April to June. In the breeding season of each year, between six and nine capture sessions were carried out. At each capture session, we exhaustively sampled all the water bodies (around 500 rocky pools and residual puddles) located along the surveyed river segment. The time required to sample the whole river segment in a single session varied from 1 to 2 d. Three ontogenetic stages were considered: juveniles (captured after their first overwintering), subadults (captured after their second overwintering), and sexually mature adults (captured after at least three overwinterings). The snout–vent length ranged 22–29 mm in juveniles, 30–34 mm in

subadults, and 35–52 mm in adults. In this species, toads reach sexual maturity at the age of 3 (Plytycz and Bigaj 1993). Gender was identified on the basis of larger forearm size and the presence of nuptial pads in males (Kyriakopoulou-Sklavounou et al. 2012). We identified each individual by the specific black-and-yellow mottled pattern on its belly, which was recorded in photographs. We used the pattern-matching software Extract Compare (Hiby and Lovell 1990) to avoid misidentification.

## Multievent capture–recapture model

The CR data set was modeled by extending the version of the robust design proposed by Cayuela et al. (2014). The model followed the typical structure of “standard” robust design (Kendall et al. 1997), which is made up of two nested levels of capture occasions. “Secondary sessions” include field sessions carried out in the same year. “Primary periods” correspond to yearly sessions (encompassing several secondary sessions), during which we assumed that the state of an individual was fixed and transitions between states were therefore considered between primary periods only. Because sex identification was not possible for juveniles and subadults (due to absence of nuptial pads in males), we used a multievent CR (Pradel 2005) approach to cope with the uncertainty in sex assignment, and sex was thus coded as a state instead of as a group as is usual in CR data modeling. From an initial departure state, individuals could be categorized as 1 of 11 distinct states: (UM) unborn male, (UF) unborn female, (JM) live juvenile male, (JF) live juvenile female, (SM) live subadult male, (SF) live subadult female, (BM) live sexually mature breeding male, (BF) live sexually mature breeding female, (NBM) live sexually mature non-breeding male, (NBF) live sexually mature nonbreeding female, (†) dead individual.

The model was conditional on the first capture occasion and an “unborn individual” state was included, which allowed us to model the probability that an individual that had not yet been observed in the population recruited as a juvenile between two primary periods. Ten state transitions (detailed in Table 1) were possible for the recruitment and ontogenetic stage transition
probabilities between two primary periods as fol-
lowing:

Matrix (1)

Given that the parameters $\phi^{\text{BNM}}$ and $\phi^{\text{BNF}}$ were not identifiable, $\phi^{\text{BM}}/\phi^{\text{BNM}}$ and $\phi^{\text{BF}}/\phi^{\text{BNF}}$ were constrained to be equal in the model. Survival transition probabilities were fixed at 1 between secondary sessions.

$$
\begin{pmatrix}
1-\alpha^{\text{JM}} & 0 & \alpha^{\text{JM}} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 1-\alpha^{\text{IF}} & 0 & \alpha^{\text{IF}} & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & \beta^{\text{JM}} & 0 & 1-\beta^{\text{JM}} & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & \beta^{\text{IF}} & 0 & 1-\beta^{\text{IF}} & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & \beta^{\text{SM}} & 0 & 1-\beta^{\text{SM}} & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & \beta^{\text{SF}} & 0 & 1-\beta^{\text{SF}} & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1
\end{pmatrix}
\quad (1)
$$

Given that we examined recruitment without accounting for gender-dependency, $\alpha^{\text{JM}}/\alpha^{\text{IF}}$ were constrained to be equal (hereafter the parameter is shown as $\alpha$). Moreover, age transition probabilities $\beta^{\text{JM}}/\beta^{\text{IF}}/\beta^{\text{SM}}/\beta^{\text{SF}}$ were fixed at 0 between secondary sessions and at 1 between primary periods.

After having recruited, individuals could suc-
cessively transition between three ontogenetic
stages, i.e., juvenile, subadult, or sexually mature
adult, given that they survived. As is usual in a
CR framework, survival estimates include both
death and permanent emigration: i.e., apparent
survival. Ten state transitions (detailed in Ta-
ble 1) for survival were possible as following:

Matrix (2)

When an individual was sexually mature,
we assumed that it could breed or skip breed-
ing each year (Cayuela et al. 2014). As individ-
uals were captured only in aquatic breeding
sites, nonbreeding toads were considered as
not available for capture and shifted to an un-
observable state (for modeling unobservable
states, see Kendall and Nichols 2002). In the
model, transitions between breeding and non-
breeding states were assumed to obey a Markov
chain process. Four state transitions (detailed in
Table 1) for breeding or nonbreeding were pos-
sible as following:

$$
\begin{pmatrix}
1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & \phi^{\text{JM}} & 0 & 0 & 0 & 0 & 0 & 0 & 1-\phi^{\text{JM}} & 0 \\
0 & 0 & 0 & \phi^{\text{IF}} & 0 & 0 & 0 & 0 & 0 & 1-\phi^{\text{IF}} & 0 \\
0 & 0 & 0 & 0 & \phi^{\text{SM}} & 0 & 0 & 0 & 0 & 1-\phi^{\text{SM}} & 0 \\
0 & 0 & 0 & 0 & 0 & \phi^{\text{SF}} & 0 & 0 & 0 & 1-\phi^{\text{SF}} & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & \phi^{\text{BM}} & 0 & 0 & 1-\phi^{\text{BM}} & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & \phi^{\text{BF}} & 0 & 1-\phi^{\text{BF}} & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & \phi^{\text{NBM}} & 0 & 1-\phi^{\text{NBM}} & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \phi^{\text{NBF}} & 1-\phi^{\text{NBF}} & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0
\end{pmatrix}
\quad (2)
$$
$$ \text{Matrix (3)} \qquad \begin{pmatrix} 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & \gamma^{\text{BM}} & 0 & 1-\gamma^{\text{BM}} & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & \gamma^{\text{BF}} & 0 & 1-\gamma^{\text{BF}} & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \gamma^{\text{NBM}} & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1-\gamma^{\text{NBF}} & 0 & \gamma^{\text{NBF}} & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \end{pmatrix} \qquad (3) $$

The transition probabilities $\gamma^{\text{BM}}/\gamma^{\text{BF}}$ and $\gamma^{\text{NBM}}/\gamma^{\text{NBF}}$ were fixed at 1 between secondary sessions.

Concerning field observation, i.e., events *sensu* multievent modeling formulation, six observation probabilities (detailed in Table 1) were considered as following

Matrix (4)

$$ \begin{pmatrix} 1 & 0 & 0 & 0 & 0 \\ 1 & 0 & 0 & 0 & 0 \\ 1-p^{\text{IM}} & p^{\text{IM}} & 0 & 0 & 0 \\ 1-p^{\text{IF}} & p^{\text{IF}} & 0 & 0 & 0 \\ 1-p^{\text{SM}} & 0 & p^{\text{SM}} & 0 & 0 \\ 1-p^{\text{SF}} & 0 & p^{\text{SF}} & 0 & 0 \\ 1-p^{\text{BM}} & 0 & 0 & p^{\text{BM}} & 0 \\ 1-p^{\text{BF}} & 0 & 0 & 0 & p^{\text{BF}} \\ 1 & 0 & 0 & 0 & 0 \\ 1 & 0 & 0 & 0 & 0 \\ 1 & 0 & 0 & 0 & 0 \end{pmatrix} \qquad (4) $$

Note that as nonbreeding individuals were not available for recapture, their recapture probability was fixed at 0.

## Biological scenarios using the E-SURGE program

This parameterization was implemented in the E-SURGE program (Choquet et al. 2009). Competitive models were ranked through a model selection procedure using Akaike information criteria adjusted for a small sample size and their derivate quantities (QAICc).

Hypotheses concerning capture and state transition probabilities were tested in an umbrella model [$\alpha$(YEAR), $\phi$(AGE $\times$ DRY), $\gamma$(SEX), $p$(-

YEAR + AGE + SEX)]. We built 32 competing models considering all the possible combinations of factors on survival, breeding, and recapture probabilities. We tested whether survival probability differed between ontogenetic stages (AGE) by specifying state transition differences between the three stages coded as states in the model. We also examined whether survival probability varied according to drought (DRY) by including a time-specific effect in the model; transitions between the primary period 2011 (a drought year) and 2012 were specified as different from the others. In terms of breeding probability, we tested whether or not breeding/nonbreeding transitions varied according to gender by considering state transition differences between genders coded as states in the model. We also examined if recapture probability varied according to year (YEAR), gender (SEX), or ontogenetic stage (AGE). Initial-state probability IS was consistently held to be different between ontogenetic stages and gender. Moreover, we only considered year-specific recruitment probability $\alpha$.

From the best-supported model, we derived the number of recruited juvenile females at $t$ per breeding female at $t-1$ per year by using the number of individuals captured in each age class and their capture probability. The confidence interval of these estimates was calculated using parametric bootstrapping. We used this recruitment as a measure of female fecundity in matrix model simulations.

## Simulation matrix model

To examine how future changes in drought frequency might influence population persistence, we built a three age-class, female-dominant, and
<table><caption>Table 1. Description of the parameters of the multievent capture-recapture model.</caption><thead><tr><th>Parameter</th><th>Definition</th></tr></thead><tbody><tr><td>α<sup>IM</sup></td><td>Probability that a newly metamorphosed male would be recruited (Matrix 1, line 1)</td></tr><tr><td>α<sup>JF</sup></td><td>Probability that a newly metamorphosed female would be recruited (Matrix 1, line 2)</td></tr><tr><td>β<sup>IM</sup></td><td>Probability that a juvenile male would not reach subadult stage (Matrix 1, line 3)</td></tr><tr><td>β<sup>JF</sup></td><td>Probability that a juvenile female would not reach subadult stage (Matrix 1, line 4)</td></tr><tr><td>β<sup>SM</sup></td><td>Probability that a subadult male would not reach adult stage (Matrix 1, line 5)</td></tr><tr><td>β<sup>SF</sup></td><td>Probability that a subadult female would not reach adult stage (Matrix 1, line 6)</td></tr><tr><td>φ<sup>IM</sup></td><td>Probability that a juvenile male would survive (Matrix 2, line 3)</td></tr><tr><td>φ<sup>JF</sup></td><td>Probability that a juvenile female would survive (Matrix 2, line 4)</td></tr><tr><td>φ<sup>SM</sup></td><td>Probability that a subadult male would survive (Matrix 2, line 5)</td></tr><tr><td>φ<sup>SF</sup></td><td>Probability that a subadult female would survive (Matrix 2, line 6)</td></tr><tr><td>φ<sup>BM</sup></td><td>Probability that a sexually mature male would survive, given it has bred (Matrix 2, line 7)</td></tr><tr><td>φ<sup>BF</sup></td><td>Probability that a sexually mature female would survive, given it has bred (Matrix 2, line 8)</td></tr><tr><td>φ<sup>NBM</sup></td><td>Probability that a sexually mature male would survive, given it has skipped breeding (Matrix 2, line 9)</td></tr><tr><td>φ<sup>NBF</sup></td><td>Probability that a sexually mature female would survive, given it has skipped breeding (Matrix 2, line 10)</td></tr><tr><td>γ<sup>BM</sup></td><td>Probability that a sexually mature male bred at time t, given it has bred at t - 1 (Matrix 3, line 7)</td></tr><tr><td>γ<sup>JF</sup></td><td>Probability that a sexually mature female bred at time t, given it has bred at t - 1 (Matrix 3, line 8)</td></tr><tr><td>γ<sup>NBM</sup></td><td>Probability that a sexually mature male skipped a breeding opportunity at time t, given it skipped breeding at t - 1 (Matrix 3, line 9)</td></tr><tr><td>γ<sup>NBF</sup></td><td>Probability that a sexually mature female skipped a breeding opportunity at time t, given it skipped breeding at t - 1 (Matrix 3, line 10)</td></tr><tr><td>p<sup>IM</sup></td><td>Probability that a juvenile male was recaptured during a secondary session and was observed as a juvenile (sex nonidentified in the field) (Matrix 4, line 3)</td></tr><tr><td>p<sup>JF</sup></td><td>Probability that a juvenile female was recaptured during a secondary session and was observed as a juvenile (sex nonidentified in the field) (Matrix 4, line 4)</td></tr><tr><td>p<sup>SM</sup></td><td>Probability that a subadult male was recaptured during a secondary session and was observed as a subadult (sex nonidentified in the field) (Matrix 4, line 5)</td></tr><tr><td>p<sup>SF</sup></td><td>Probability that a subadult female was recaptured during a secondary session and was observed as a subadult (sex nonidentified in the field) (Matrix 4, line 6)</td></tr><tr><td>p<sup>BM</sup></td><td>Probability that a sexually mature male (sex identified in the field) was recaptured during a secondary session (Matrix 4, line 7)</td></tr><tr><td>p<sup>BF</sup></td><td>Probability that a sexually mature female (sex identified in the field) was recaptured during a secondary session (Matrix 4, line 7)</td></tr></tbody></table>

prebreeding Leslie matrix (juveniles, subadults, and adults) (Caswell 2001):

$$ \text{Matrix (5)} \quad \begin{pmatrix} 0 & 0 & F * BP \\ S_1 & 0 & 0 \\ 0 & S_2 & S_3 \end{pmatrix} \quad (5) $$

We used the survival probability ($S_1$, juvenile survival; $S_2$, subadult survival; $S_3$, adult survival) from the previous CR data analysis. Fecundity was possible only for adult individuals and consisted of the recruitment estimations (F) (number of recruited juvenile females at $t$ per breeding female at $t-1$) multiplied by the proportion of breeding females BP. The proportion of breeding females was obtained using the stable stage distribution of a two-stage matrix (breeding and nonbreeding females) with transitions between states estimated in the CR data modeling step.

We built stochastic projections based on two matrices: one on the demographic parameter estimates of “standard” years, and one on the demographic parameters of “drought” years. The frequency of drought events was then increased from 1/10 yr to 1 per year. We simulated 1000 population trajectories over 10,000 yr ($k$) using the stochastic process and for each simulation calculated the population growth rate using $log(t_k) - log(t_0)/k$. We derived the median growth rate and its 95% confidence interval using the quantiles of the resulting population growth rate distribution.

## RESULT

During the survey, we carried out a total of 7827 captures (3630 males, 1659 females, 1184 subadults, and 1354 juveniles), of which 768
<table><caption>Table 2. Variation of *Bombina variegata* vital rates in a population in southeastern France (Bourge River); model selection procedure: *r* = model rank, *k* = number of parameters, *dev* = residual deviance, QAICc = Akaike information criteria adjusted for a small sample size and their derivate quantities, AGE = variation between the three ontogenetic stages (juvenile, subadult, and adult), DRY = drought, YEAR = variation between years, CST = constant.</caption><thead><tr><th>r</th><th>Survival</th><th>Breeding</th><th>Recruitment</th><th>Recapture</th><th>k</th><th>Dev</th><th>QAICc</th></tr></thead><tbody><tr><td>1</td><td>AGE × DRY</td><td>SEX</td><td>YEAR</td><td>YEAR + AGE + SEX</td><td>24</td><td>21867.77</td><td>21915.93</td></tr><tr><td>2</td><td>AGE × DRY</td><td>CST</td><td>YEAR</td><td>YEAR + AGE + SEX</td><td>22</td><td>21875.12</td><td>21919.24</td></tr><tr><td>3</td><td>AGE</td><td>SEX</td><td>YEAR</td><td>YEAR + AGE + SEX</td><td>21</td><td>21886.96</td><td>21929.07</td></tr><tr><td>4</td><td>AGE</td><td>CST</td><td>YEAR</td><td>YEAR + AGE + SEX</td><td>19</td><td>21894.06</td><td>21932.16</td></tr><tr><td>5</td><td>DRY</td><td>CST</td><td>YEAR</td><td>YEAR + AGE + SEX</td><td>18</td><td>21925.39</td><td>21961.47</td></tr><tr><td>6</td><td>DRY</td><td>SEX</td><td>YEAR</td><td>YEAR + AGE + SEX</td><td>20</td><td>21923.47</td><td>21963.58</td></tr><tr><td>7</td><td>CST</td><td>CST</td><td>YEAR</td><td>YEAR + AGE + SEX</td><td>17</td><td>21930.53</td><td>21964.61</td></tr><tr><td>8</td><td>CST</td><td>SEX</td><td>YEAR</td><td>YEAR + AGE + SEX</td><td>19</td><td>21928.43</td><td>21966.52</td></tr><tr><td>9</td><td>AGE × DRY</td><td>SEX</td><td>YEAR</td><td>YEAR</td><td>21</td><td>22285.08</td><td>22327.20</td></tr><tr><td>10</td><td>AGE × DRY</td><td>CST</td><td>YEAR</td><td>YEAR</td><td>19</td><td>22291.56</td><td>22329.66</td></tr><tr><td>11</td><td>AGE</td><td>SEX</td><td>YEAR</td><td>YEAR</td><td>18</td><td>22302.97</td><td>22339.05</td></tr><tr><td>12</td><td>AGE</td><td>CST</td><td>YEAR</td><td>YEAR</td><td>16</td><td>22311.30</td><td>22343.36</td></tr><tr><td>13</td><td>DRY</td><td>CST</td><td>YEAR</td><td>YEAR</td><td>15</td><td>22343.78</td><td>22373.84</td></tr><tr><td>14</td><td>DRY</td><td>SEX</td><td>YEAR</td><td>YEAR</td><td>17</td><td>22340.32</td><td>22374.40</td></tr><tr><td>15</td><td>CST</td><td>CST</td><td>YEAR</td><td>YEAR</td><td>14</td><td>22349.20</td><td>22377.25</td></tr><tr><td>16</td><td>CST</td><td>SEX</td><td>YEAR</td><td>YEAR</td><td>16</td><td>22345.55</td><td>22377.62</td></tr><tr><td>17</td><td>AGE × DRY</td><td>SEX</td><td>YEAR</td><td>AGE + SEX</td><td>21</td><td>21899.63</td><td>21941.75</td></tr><tr><td>18</td><td>AGE × DRY</td><td>CST</td><td>YEAR</td><td>AGE + SEX</td><td>19</td><td>21904.88</td><td>21942.97</td></tr><tr><td>19</td><td>AGE</td><td>CST</td><td>YEAR</td><td>AGE + SEX</td><td>16</td><td>21924.69</td><td>21956.76</td></tr><tr><td>20</td><td>AGE</td><td>SEX</td><td>YEAR</td><td>AGE + SEX</td><td>18</td><td>21917.63</td><td>21953.71</td></tr><tr><td>21</td><td>DRY</td><td>CST</td><td>YEAR</td><td>AGE + SEX</td><td>15</td><td>21955.26</td><td>21985.31</td></tr><tr><td>22</td><td>DRY</td><td>SEX</td><td>YEAR</td><td>AGE + SEX</td><td>17</td><td>21953.24</td><td>21987.32</td></tr><tr><td>23</td><td>CST</td><td>CST</td><td>YEAR</td><td>AGE + SEX</td><td>14</td><td>21960.93</td><td>21988.98</td></tr><tr><td>24</td><td>CST</td><td>SEX</td><td>YEAR</td><td>AGE + SEX</td><td>16</td><td>21965.20</td><td>21994.25</td></tr><tr><td>25</td><td>AGE × DRY</td><td>SEX</td><td>YEAR</td><td>CST</td><td>17</td><td>22133.04</td><td>22167.12</td></tr><tr><td>26</td><td>AGE × DRY</td><td>CST</td><td>YEAR</td><td>CST</td><td>15</td><td>22139.15</td><td>22169.21</td></tr><tr><td>27</td><td>AGE</td><td>SEX</td><td>YEAR</td><td>CST</td><td>14</td><td>22151.78</td><td>22179.83</td></tr><tr><td>28</td><td>AGE</td><td>CST</td><td>YEAR</td><td>CST</td><td>12</td><td>22159.58</td><td>22183.62</td></tr><tr><td>29</td><td>DRY</td><td>CST</td><td>YEAR</td><td>CST</td><td>11</td><td>22190.76</td><td>22212.80</td></tr><tr><td>30</td><td>DRY</td><td>SEX</td><td>YEAR</td><td>CST</td><td>13</td><td>22187.16</td><td>22213.21</td></tr><tr><td>31</td><td>CST</td><td>CST</td><td>YEAR</td><td>CST</td><td>10</td><td>22196.51</td><td>22216.54</td></tr><tr><td>32</td><td>CST</td><td>SEX</td><td>YEAR</td><td>CST</td><td>12</td><td>22191.75</td><td>22216.79</td></tr></tbody></table>

individuals were identified (328 males, 237 females, 73 subadults, and 130 juveniles; state at last capture). The number of juveniles identified each year varied from 36 (in 2011: drought conditions) to 145 (in 2010), the number of subadults from 8 (in 2010) to 132 (in 2011), the number of females from 94 (in 2011) to 139 (in 2014), and the number of males from 150 (in 2011) to 221 (in 2014).

The model selection procedure is provided in Table 2. The best-supported model [$\alpha$(YEAR), $\phi$(AGE × DRY), $\gamma$(SEX), $p$(YEAR + AGE + SEX)] (deviance = 32893.87, QAICc = 32938.51, QAICc relative weight = 0.91) suggested that recapture

probability depended on gender, ontogenetic stage, and year (Fig. 2a). The model also indicated that breeding propensity varied between genders (Fig. 2b). The probability of breeding in two consecutive years was slightly higher in males than in females, while the probability of skipping breeding for two consecutive years was lower in males than in females. Survival probability increased with age, but was negatively influenced by drought at the juvenile and adult stages (Fig. 2c). The survival rate fell by 12% in juveniles and 10% in adults when the population experienced a severe drought. Fecundity was slightly lower during a drought event than
<figure><img src="image_5.png" /></figure>

Fig. 2. Demographic parameters (mean and 95% CI) in a population of *Bombina variegata* in southern France: (a) recapture probability: circle = juvenile, triangle = subadult, cross = adult female, square = adult male, (b) breeding probability: empty circle = probability of a male breeding two consecutive times, empty square = probability of a female breeding two consecutive times, full circle = probability of a male skipping breeding two consecutive times, full square = probability of a female skipping breeding two consecutive times, (c) survival probability at three ontogenetic stages (J = juvenile, S = subadult, A = adult) in standard conditions (blue) and during a drought (orange), (d) Fecundity in standard conditions (blue) and during a drought (orange), (e) predicted population growth rate according to drought frequency (from 1 drought every year to 1 drought every 10 yr).

in normal conditions (Fig. 2d). Fecundity fell by 31% when the population experienced a severe drought.

Stochastic matrix models (Fig. 2e) predicted a 17% increase in the population growth rate from the most pessimistic scenario in which one drought is experienced every year to the most optimistic scenario of one drought per decade.

DISCUSSION

Our findings demonstrate that severe drought has an impact both on fecundity and survival at different ontogenetic stages in *B. variegata*.

Simulations show that population growth is strongly influenced by drought frequency, which is an alarming warning sign for population persistence of this endangered amphibian.

*Fecundity is impacted by drought events*

Our results indicate that fecundity is lower during droughts than in normal conditions; fecundity fell by 31% when the population experienced a severe drought. This decrease is likely the result of the direct effect of drought on tadpole mortality due to the desiccation of breeding sites. In *B. variegata*, high mortality rates during larval growth are usually a
consequence of hydroperiod unpredictability of oviposition sites (Morand 1997). In amphibians in general, a high deficit of rainfall leads to catastrophic breeding failure (Richter et al. 2003, Taylor et al. 2006), which may in turn strongly affect population dynamics and persistence (Scheele et al. 2012). This decrease in fecundity could also be the result of density-dependent factors (which affect larval survival, larval size, and time of metamorphosis) due to the reduction of available breeding sites during a drought leading to a concentration of tadpoles (Berven 1990). Likewise, an increase in water temperature and unstable water level of a breeding site during a drought could accelerate the development rate and lead to a lower growth rate at the larval stage. This usually results in a reduction of lipid levels and body size at metamorphosis, which leads to lower survival rates over the first overwintering (Scott et al. 2007, Tejedo et al. 2010, Schmidt et al. 2012).

*Survival probability is impacted by age and drought*

Our findings also show that survival is negatively influenced by drought at the juvenile and adult stages while no detrimental effect has been detected on subadult survival, likely because of a lack of statistical power (only 73 subadults in our data set). The survival rate fell by 12% in juveniles and by 10% in adults when the population experienced a severe drought. Such a deleterious effect has also been reported in frogs (Berven 1990) and salamanders (Church et al. 2007). This confirms the conclusions of a previous study in which we highlighted the adverse effect of rainfall deficit (that also occurred in spring 2011) on adult survival in *B. variegata* in a population in northeastern France (Cayuela et al. 2014). Severe dehydration has detrimental effects on the physiological systems, organs, and tissues of amphibians, affecting for instance the capacity of muscle contraction, locomotion, and aerobically supported activities (Hillman 2009). Pulmonary compliance and lung volume can facilitate lymph mobilization, which appears to be a key adaptation of anurans to dehydration because it stabilizes plasma volumes (Hillman et al. 2010). However, in bombinatorids, small lung and lymph volumes resulting from a small body

mass likely limit the species' ability to cope with severe dehydration (Withers et al. 2014). In *B. variegata*, this limitation is usually mitigated by microhabitat selection (springs and small cool streams) during the drier episodes of their annual breeding period (Beshkov and Jameson 1980, Pichenot 2008). However, in combination with a decrease in soil moisture, the desiccation of these microhabitats likely contributes to low survival rates during severe droughts.

*Population growth rate is impacted by drought frequency*

The stochastic matrix model predicts that potential changes in drought frequency will strongly affect the population growth rate. In this model, the population growth rate increases by 17% from the most pessimistic scenario in which one drought is experienced every year, to the most optimistic scenario of one drought per decade. Simulations using ECHAM4 and HadCM3 climate models predict that the frequency of droughts with an intensity of today's 100-yr events is likely to be more than one every 40 yr in the 2020s, and more than one every 10 yr in the 2070s in a large part of western and southern France (Lehner et al. 2006). As a result, it is likely that *B. variegata* populations located in western and southern France will experience a marked decline with a potential risk of local extinction. To test this prediction, we strongly encourage that future studies couple demographic processes and historical, present and predicted habitat suitability (using species distribution models) in order to fully understand the demographic response of this endangered amphibian to drought frequency and variation in soil moisture.

*Conservation recommendations*

Our results indicate that severe droughts in Western Europe can constitute a potential threat for *B. variegata* populations, which have experienced a significant decline during the last century (Massemin and Cheylan 2001, Lescure et al. 2011). We now propose conservation advances to guide management actions of conservationists.

Reducing exposure to stressful dehydration conditions is usually critical in mitigating
amphibian vulnerability and the impact of cli-
mate change on these vertebrates (Shoo 2011).
*Bombina variegata* often use springs and small cool
streams during the drier periods of the breeding
season (Beshkov and Jameson 1980). These influ-
ence body temperature and control dehydration
rates. Limiting the loss of water in an ecosystem,
for example, by water retention, slowing water
flow, reducing evapotranspiration, or bringing
more water to ecosystems, e.g., by restoring hy-
draulic connections, could mitigate the detri-
mental effects of dehydration during droughts.
Our conservation action recommendations are in
line with these aims.

First, we strongly discourage draining opera-
tions in actively managed woodlands, wetland
meadows used for grazing, and bogs, as this
could reduce the availability of microclimate
refuges. If draining is already taking place,
the drains can be filled with mud. If draining
operations cannot be avoided, the flow can be
reduced with small dams in order to capture
water during the wet season for later release
during the dry season. The same result can be
obtained if channels are coupled with irriga-
tion systems in the vicinity of breeding sites.
These storage devices should be located under
canopy cover and close to natural springs and
streams in order to maintain cool, moist condi-
tions. Second, we stress the importance of veg-
etation cover, e.g., fringing riparian forests, in
limiting evapotranspiration (Olson et al. 2007).
In addition, large fallen logs provide a diver-
sity of thermic refuges for amphibians, which,
by limiting the desiccation risk, facilitate high
species richness, especially in a floodplain
area (Indermaur et al. 2009). In actively man-
aged woodlands, harvesting should be limited
in areas close to natural springs and streams
(within a distance of 40–100 m) in order to
preserve interstitial spaces in substrates, i.e.,
microhabitats (Olson et al. 2007). Third, rein-
stating or maintaining hydraulic connections is
key. If springs are exploited for human water
consumption, or the water is channeled off for
irrigation, a common practice along the rivers
of southeastern France, a reasonable propor-
tion of the water volume should be left in the
natural ecosystem.

Furthermore, studies showed landscape alter-
ation may hinder or reverse the expected pop-

ulation response to changed climate conditions (Selwood et al. 2015). Populations in modified landscapes may decline faster than expected with the added pressures arising from climate change. For instance, Mantyka-Pringle et al. (2012) demonstrated that areas with high current temperatures and where average rainfall has decreased over time exacerbate the negative effects of habitat loss and fragmentation on species density. In addition to direct conservation actions, we thus encourage the suppression of additional stresses as habitat loss and fragmentation (see Cayuela et al. 2015), which could have, in combination with drought, synergistic detrimental effects on population dynamics of *B. variegata*.

ACKNOWLEDGMENTS

We thank all the fieldworkers who helped with
data collection. We are also grateful for the technical
support provided by the Parc Naturel des Monts
d'Ardèche. This research was funded by the Rhône-
Alpes DREAL, the Conseil Général d'Ardèche and
the Agence de l'Eau Rhône-Alpes. Toad capture was
authorized by the Préfecture de l'Ardèche (arrêté
no. 2014-288-002).

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