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Keywords:

  • demography;
  • dispersal;
  • survival;
  • transience;
  • Triturus alpestris

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • 1
    This work aims to illustrating how it is possible to measure different modalities of adult dispersal in two subdivided populations of the alpine newt (Triturus alpestris). Recent developments in capture–mark–recapture methods make it possible to estimate transience rates from individuals captured only once. In the context of subdivided newt populations, transience is assumed to express nomadic behaviour that contribute to breeding dispersal. Skeletochronology and recaptures within each pond system also made it possible to estimate emigration rates and local dispersal.
  • 2
    Two subdivided populations of alpine newts were monitored over 4 and 5 years, respectively. Whereas population A is suspected to have been established for more than 100 years, population B was monitored when colonizing a newly created archipelago of ponds.
  • 3
    Transience was detected in each population at similar rates (37% in population A and 35% in the population B). Annual apparent survival rates were estimated as 82% in population A vs. 33% in population B. Similarity of age structures between populations leads us to consider such low survival rates in population B as resulting from emigration. Emigration was thus negligible in population A and was estimated to reach 57·3% in population B. Conversely, high local dispersal (movements within a pond system) was detected in population A, but not in population B.
  • 4
    Even though the causation of dispersal in newts (genetic polymorphism vs. phenotypic plasticity) remains unexplored, our study succeeded in identifying several dispersal components that could result from different selective pressures (habitat heterogeneity at different temporal scales). Experimental approaches are needed to investigate the causative bases of these traits.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

According to the metapopulation concept, migration among local populations and colonization of empty habitat patches are the main processes that prevent or compensate for local extinctions (Stacey, Taper & Johnson 1997). The dynamics of a population at the local scale then tightly depends on demographic connections with other local populations (Brown & Kodric-Brown 1977; Hanski 1999). However, despite great theoretical interest, most literature in this field remained uncoupled from empirical research because of the scarcity of in situ estimations of dispersal rates (Hanski 2001). Assessing dispersal rates by using direct methods indeed remains a difficult task for which the capture–mark–recapture method (CMR) still appears to be a promising approach (Pollock et al. 1995; Bennetts et al. 2001). However, accounting for dispersal in multi-site CMR models (Brownie et al. 1993; Spendelow et al. 1995) has long been considered impracticable because of a lack of realism and the high variance due to the large number of parameters that have to be used (Crochet 1996; Ims & Yoccoz 1997). On the other hand, the CMR method when applied to single local populations usually cannot separate surviving individuals that definitively leave the study site from those that die (Jolly 1965; Seber 1965). Dispersal rates are consequently included in mortality rates. This remains true as long as only one type of dispersal occurs in the population. Although most individuals are faithful to the same site, others exhibit nomadic behaviour that leads to their fugitive presence in any habitat patch. When there is a mixture of such transience (individuals that are caught only once) and residency (individuals that are caught several times) in a sample of individuals, it becomes possible to quantitatively investigate properties of these two behavioural categories (Lebreton 1995; Pradel et al. 1997). Pradel et al. (1997) proposed a method to discriminate between the proportion of transient individuals (those with no apparent local survival) and the survival rates of residents. This method makes it possible to identify the existence of individuals with year-to-year dispersal behaviour and to estimate their frequencies, thus gaining a better estimation of demographic parameters.

Newt populations have been among the first subjected to empirical approaches in the context of metapopulation concepts because breeding occurs in ponds that are in essence patchily distributed in the landscape (Gill 1978, 1979; Joly et al. 2001). Moreover, habitat ponds experience rapid ecological successions that lead to their disappearances through their in-filling with alluvial deposits. Whereas natal dispersal is high (Joly & Grolet 1996), breeding dispersal is also expected to contribute significantly to metapopulation dynamics (Miaud, Joly & Castanet 1993). However, fidelity to the breeding site can also be strong in adult newts (Twitty 1959; Gill 1979; Joly & Miaud 1989b). Because of such behavioural variation, newt populations could provide suitable conditions for empirical studies on dispersal in patchy populations. Our aim was thus to estimate survival rates in two newt populations by taking into account transience rates. Both the CMR method and age structure analysis were used to distinguish components of breeding dispersal, such as transience (i.e. individuals that were captured only once throughout the study), local dispersal (movements within each pond archipelago), and emigration (dispersal of non-transient individuals out of the studied pond archipelago). We first investigated the prevalence of transience, which can be considered a reliable estimator of nomadic behaviour, thus excluding any bias due to handling or marking. We then estimated adult emigration and local dispersal. Finally, we estimated survival rates by taking into account the frequency of transient individuals.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

population monitoring

Two subdivided populations of alpine newts (Triturus alpestris) were monitored in sites located 60 km apart. Each site consisted of an archipelago of cattle ponds within a wood–pasture matrix that enables newts to migrate between ponds and forest, and from one pond to another. We monitored population A at four ponds, located in the Bresse lowlands (80 km north of Lyon, 5°11′ E, 46°14′ N). These ponds were dug approximately 100 years ago in pastures near the edge of a forest. Pond areas varied from 40 m2 to 100 m2, and depths from 0·80 m to 1·50 m. The maximum distance between ponds was 200 m. Population B was monitored at five ponds located on the Dombes Plateau (20 km northeast of Lyon, 4°55′30″ E, 45°57′ N) in the estate of Pierre Vérots Foundation (private natural reserve). The ponds have been excavated in pastures close to forests in September 1992. Pond morphology was standardized to 50-m2 area and 1·5-m maximum depth to maximize netting efficiency. The maximum distance between ponds was 500 m (see Miaud et al. 1993 and Joly & Grolet 1996 for complete descriptions of each site).

At site A, monitoring took place from 1987 to 1990. At site B, the present study covers the period 1993–97, a period that followed pond building. At site A, we caught the newts with a dip-net according to a standardized sampling duration (1 h per pond) whereas at site B we used a fishnet with two repeated samples at each session. Every spring we conducted three capture sessions separated by 4-week interval (i.e. samplings occurred in March, April and May) to give each individual an equivalent chance of being captured at least once, given individual variability in the timing of breeding. The three samples were then pooled to constitute a single annual sample. Robust design was not applied because of the potential negative impacts of repeated netting on the aquatic habitats. Newts were individually marked using tattooing (Joly & Miaud 1989a) at site A, and PIT-tagging (TROVAN system: Faber 1997) at site B. These techniques have been shown not to differ in their impact on capture probability, survival and condition (Jehle & Hödl 1998; Perret & Joly 2002).

age structures

Individual ages were established by skeletochronology applied to the longest bone of the largest hindleg digit (Castanet, Meunier & Ricqlès 1977; Miaud 1992). Finger sampling was authorized by French services in charge of nature preservation and ethics, such as the Environment Ministry as well as local administrations. We conducted toe sampling after the newts were anaesthetized. Sampling of one digit does not affect survival (Clarke 1972) and the toe totally regenerates within one year. We performed a generalized linear model using SAS package (SAS Institute 2000) to test for difference in age structures between populations according to sex. Because we detected overdispersed data when using a Poisson distribution, we compared a multinomial model using cumulated logit (Agresti 1999) and a polynomial model to test for relationships between population, sex and age frequencies. The multinomial model uses a maximum likelihood-based approach, and performs a chi-square test on the likelihood ratio to evaluate the significance of each effect (McCullagh & Nelder 1989). Akaike information criterion (AIC) (Akaike 1973) revealed that the multinomial model fitted the data better (AIC = –438·20 with 10 parameters) than the polynomial model (AIC = –437·76 with 12 parameters). We then used the multinomial model for evaluating the impact of sex and population on age structures. We used a non-sequential procedure to test the significance of each effect (sex, population, and their interaction) so to avoid effects of ordering within the model. Non-significant terms were successively removed to obtain the final model.

modelling demographic parameters

We followed a procedure based on the capture histories of marked individuals for analysing survival on a yearly basis (Lebreton et al. 1992). As a starting point, we selected the general CJS model (Cormack 1964; Jolly 1965; Seber 1965) constructed separately for each sex in each population. For each population, this model allows survival (φ) and capture probabilities (p) to vary relative to sex (s) and time (t). It is denoted (φs×tps×t). As a general rule, an (*) represents an interaction between variables (Lebreton et al. 1992). We organized the sampling protocol to meet the five assumptions of CJS models. Finally, the assumptions can be tested by means of goodness-of-fit (GOF) test such as the one implemented in the software RELEASE (Burnham et al. 1987). In its modified version (Pradel 1993), this test comprises four components that are more or less sensitive to one or the other assumptions (Test3.SR, Test3.SM, Test2.CT and Test2.CM). The component Test3.SR is of particular interest to us as it should specifically detect transient behaviour in monitored individuals. In fact, if the presence of transients is the only violation of assumptions, this component should be the only one affected in a predictable way. Indeed, the test of this component is made up of a series of 2 × 2 contingency tables, one per capture occasion, contrasting the fates of previously marked and newly marked individuals captured on the same occasion according to whether they are recaptured again or not. The deviation from homogeneity induced by the presence of transients should be seen by an excess of ‘non-recaptured’ individuals amongst the newly marked ones (Lebreton 1995). Other possible causes leading to a similar pattern in the contingency tables may be an immediate increase in mortality following marking and/or a strong heterogeneity in capture rates (Cooch, Pradel & Nur 1997). If transients are detected, specific models must then be used that are described in the next subsection. Alternatively, when component tests are equally affected without any clear pattern detectable in the contingency tables, it is recommended that a variance inflation factor, c-hat, is used to scale the model deviance and to inflate parameter variances (Burnham et al. 1987; Lebreton et al. 1992). Following these authors, this factor is computed as the ratio of the overall goodness-of-fit test to its degrees of freedom. Finally, although all components may be affected, Test3.SR may be affected more. Then, the use of transient models is warranted and the variance inflation factor is to be computed as the ratio of the sum of the three other components divided by the sum of the degrees of freedom.

transience estimation

Pradel et al. (1997) have shown that a mixture of transient and resident individuals leads to capture–recapture models equivalent to classical CJS models allowing for a specific initial survival immediately after marking. They have further established that initial survival between i and i + 1 (inline image) is related to the proportion τi of transients among unmarked individuals captured at i by inline imageφi, where φi is the survival rate of residents between i and i + 1. This survival rate φi is usually estimated as the survival observed over the same period in animals marked earlier than i. τi can then be computed as:

  • image(eqn 1)

For the population as a whole, assuming that residents and transients have similar initial capture probabilities, the proportion of transients is:

  • image(eqn 2)

where Ni is the number of individuals released at i that were captured for the first time on that occasion, mi is the number of individuals released at i that have been captured previously to i. E stands for the expectation of these parameters. In practice, we will use the observed values of Ni and mi. The variance of τi derived by the delta method (Seber 1982, p. 7) is:

  • image

and that of Ti is:

  • image

Unfortunately, such a (φ*, φ) parameterization does not allow us to test whether τ is sex dependent or constant over time. One possibility is to use a log-link function instead of the classic logit function normally used in parameterization. This alternative (τ, φ) parameterization allows one to isolate parameter τ directly and to test some new models equivalent to the first parameterization for similar models (for example: inline image,φ is identical to τt, φ). This particular parameterization was applied to the best model of the φ*, φ parameterization.

The straightforward generalization of the CJS model applied separately to each sex in the presence of transients can be denoted (inline image). This model is equivalent to the model of Brownie & Robson (1983) applied separately to each sex. A goodness-of-fit test is provided by the sum of the component tests other than Test3.SR computed using programme RELEASE run with two groups (males and females) (Brownie & Robson 1983; Pradel et al. 1997). When there is no apparent deviation from the assumptions, a variance inflation factor can be computed as with the CJS model (Paradis, Guedon & Pradel 1993; Pradel, Cooch & Cooke 1995; Prévot-Julliard, Lebreton & Pradel 1998). We calculated the AIC for each proposed model from its number of parameters (np) and its scaled deviance (Dev) as AIC = Dev + 2 np. We fitted the models using programme SURGE (version 4·0; Cooch, Pradel & Nur 1997).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

population monitoring

Altogether 782 newts (389 males and 393 females) were marked at site A, and 603 (283 males and 320 females) at site B. Of 125 individuals recaptured at site A, 36 (28·8%) were recaptured in a pond that was not the pond of first capture. In contrast, no migration from one pond to another was detected at site B where all 90 recaptured individuals were caught in the pond where they had been captured first. Age structures were estimated from 131 individuals at site A, and 76 individuals at site B (Fig. 1). We did not detect any significant difference in age structures between populations (Table 1).

image

Figure 1. Age structures of each population. (a) Population A (males: n = 69; females: n = 62). (b) Population B (males: n = 35; females: n = 41).

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Table 1.  Comparison of age structures. Results of the test on the likelihood ratios after modelling the data using a multinomial model
Effectd.f.χ2 LRTP
Population1 2·800·0941
Sex113·710·0002
Population × Sex1 2·440·1186

modelling demographic parameters

The CJS model by sex did not fit the data for any site satisfactorily. However, the lack of fit was mainly due to the component Test3.SR, as was expected if transience occurred. The transient model itself (inline image) was acceptable for both sites although the model only poorly fitted the data from site A (Table 2). Some lack of fit remained for the males at this site (GOF test of model (inline image) for the males alone at site A: χ2 = 8·11, d.f. = 2, P = 0·02). Due to the limited evidence for lack of fit, we opted to apply a variance inflation factor. The variance inflation factor was calculated as: ĉ = 8·72/4 = 2·18.

Table 2.  Goodness-of-fit tests of the general capture-recapture models (degrees of freedom in brackets)
 GOF test of model φ(s × t) p(s × t) (CJS model by sex)GOF test of modelinline image (transient model by sex)Test of H1: (φ(s × t) p(s × t)) vs H0: (inline image) (RELEASE Test 3.SR)
 P P P
Population A30·35 (7)< 0·0018·72 (4)0·0721·63 (3)< 0·001
Population B19·87 (10)  0·034·68 (5)0·4615·19 (5)< 0·01

From model (inline image) we proceeded to fit models with simplified capture structures. In both populations, the Akaike information criterion pointed to a model with unique capture probability for the whole study and for both sexes (Table 3). All combinations of sex and time effects were tried to (φ*) and (φ) leading to the fit of 25 different models for each site. For population A, the model with the simplest structure (φ* φ p) was retained (Appendix 1). For site B, four models could be considered, from which we selected the most parsimonious (inline imageφt p) (Appendix 1). The initial apparent survival and the survival of residents were both time-dependent at this site. Based on these models, we tested the influence of time on φ, and of sex on τ by using the log-link function, and finally selected (φtτ p) for population B (Table 4).

Table 3.  Estimates of capture rates at each site. The deviance (DEV), the number of estimable parameters (np), and Akaike's information criterion (AIC = DEV + 2np) are given for each model. The lowest AIC were for p invariant over sex and time (in bold)
Survival model Capture model
p(s × t)p(s × t)psptp
Population ADEV366·67366·69367·24367·55367·90
inline imagenp 14 13 12 12 11
 AIC394·67392·70391·24391·55389·90
Population BDEV585·532586·329587·661586·492587·707
inline imagenp 20 17 15 17 14
 AIC625·53620·33617·66620·49615·71
Table 4.  Results of capture–recapture models for the two populations with τ
  ττs 
Population AφpDEV372·42372·31
np  5  6
AIC382·42384·31
Population BφtpDEV598·53596·01
np  6  7
AIC610·53610·01

Males and females shared the same parameters in both populations. The low recapture rates at site A (P = 0·23, Table 5) may result from difficulties in sampling newts efficiently, as high recapture probability at site B (P = 0·79, Table 5) demonstrates the efficiency of newt netting in these ponds. Survival differed markedly between the two populations. At site A, the model gave a constant high survival (φ = 0·82, Table 5), which was consistent with the age structure. Such high survival is also consistent with our knowledge of site fidelity in this population (Joly & Miaud 1989b). At site B, survival probabilities varied from one year to another. However the confidence interval for (φ94) was very large (Table 5) and has to be considered carefully. In fact, in 1994 all of the few colonists that were marked in the preceding year were recaptured and contributed to the low accuracy of the estimation of this parameter. In contrast with site A, mean survival of the last two years was lower (mean survival probability: = 0·35) suggesting either high mortality or dispersal.

Table 5.  Parameter values according to model choice in both sites (profile likelihood method used to estimate confidence interval)
 φ95% C.I.φ*95% C.I.τ95% C.I.p95% C.I.
Population A0·820·49, 0·950·430·21, 0·670·480·37, 0·580·230·12, 0·39
 Yearφt[95% conf. int]φ*[95% conf. int]*τ[95% conf. int]*p[95% conf. int]
Population B1993−94  0·090·03, 0·26    
1994−950·560·31, 0·790·320·24, 0·410·430·25, 0·610·790·62, 0·89
1995−960·410·28, 0·550·230·16, 0·32    
1996−970·290·19, 0·420·170·10, 0·26    

transience estimation

No difference between sexes was detected in the proportions of transients and residents. From the (φ* φ p) model selected for population A, 37·3% of individuals were deemed transient on average (Tmean in Table 6). From the (φt τ p) model selected for population B (test of a sex effect on τ, χ2 = 2·57, d.f. = 1, P = 0·11; Table 5), average transience rate was 35·3% (Tmean in Table 6), this value being very close to that found at site A. Transience rates had the same range of variation at the two sites, except for T1994 at site B for which confidence interval was estimated with low accuracy due to the small size of the sample that year.

Table 6.  Transience rates in the two sites
Population APopulation B
YearT95% C.I.YearT95% C.I.
19880·400·31, 0·4919940·420·24, 0·60
19890·410·32, 0·4919950·340·20, 0·49
19900·310·24, 0·3719960·280·16, 0·41
   19970·370·21, 0·52
Tmean0·37 Tmean0·35 

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Although a previous study showed that colonization was mainly by juveniles, particularly males (Joly & Grolet 1996), the present study demonstrates that adult dispersal also represents a strong component of migrations among populations, as previously suggested by Miaud, Joly & Castanet (1993). Using both recent methodological developments of CMR statistics for the estimation of survival and transience rates (Lebreton 1995; Pradel et al. 1997), and estimation of age structures, we identified three types of dispersal behaviour: transience, emigration and local dispersal. Their respective frequencies differed between the two studied populations.

transience

The similarity of transience rates between populations suggests that high breeding dispersal is a trait characteristic of alpine newt populations. Even though this original result needs to be corroborated by new observations, we are confident in the robustness of transience estimation. Transience can reasonably account for nomadic behaviour in the studied populations, suggesting high rates of breeding dispersal. Because ponds are clearly delimited habitat patches that strongly differ from the surrounding habitat matrix (water vs. land), transience cannot result from brief intrusions of newts from patches adjacent to the studied pond archipelagos. However, a part of transience may result from local dispersal at the border of each archipelago, as other ponds can be found in the near neighbourhood. Nevertheless, the distances that separated each studied pond archipelago from other ponds were greater than the distances that separated the ponds within each archipelago. Transience was not related to sex and data were too scarce to allow any comparison of age between resident and nomadic individuals. Migration by transient individuals from one population to another may considerably reduce the estimation of apparent survival (Pradel et al. 1995, 1997). Estimation of transience provides a means of improving such survival estimations. Indeed in a previous study that did not take transients into account, Miaud (1990) underestimated survival of population A by approximately 30%.

As no impact of marking on dispersal was detected, transience can be considered as a behavioural trait of the studied populations. Mean transience rates were quite similar between the two populations (37·3% at site A and 35·3% at site B). However, we suppose that transience estimation is more reliable at site B than at site A because of higher capture probabilities at this site. Estimation of transience rates and survival rates could still be refined by improving sampling designs to ensure high recapture rates and low capture heterogeneity. Age estimation of each individual during long-term monitoring would also make it possible to take into account the influence of age on capture probability and thus to improve parameter modelling. Similarity of transience rates between the two populations suggests that this trait is shared at the species level. However, the confidence intervals are too high to make inference on similarity of transience rates. Comparisons of transience rates among species differing by age structures would also open windows on the evolutionary significance of nomadic behaviour.

adult emigration and local dispersal

Survival rates differed between the two populations, ranging from 0·35 at site B (for the last two years) to 0·82 at site A. However, age structures did not significantly differ between the two sites overall. As age structures can indicate variation in survival among populations (Forester & Lykens 1991), the low level of apparent survival at site B might denote a higher emigration rate than at site A. For estimation of the emigration rate at site B, we propose deductive reasoning, while keeping in mind the strong assumptions it implies. The good match between age structure and estimated survival at site A leads us to consider that emigration of adult individuals (except transient ones) from this site was negligible. Secondly, if we assume that survival was similar at both sites and use the best estimation of survival φtrue estimated at site A, then we can empirically calculate emigration rate e at site B by replacing φ by φtrue, and φ* by φ computed for site B in eqn 1:

  • image

From this calculation, emigration should represent 57·3% of the non-transient newts each year at site B. Even if such a value has to be regarded with extreme care, the reasoning highlights behavioural variation that can occur between populations. These variations can be related to various causes such as climate, habitat, landscape or time after the establishment of the population. At the local scale of the pond archipelago, whereas short movements (local migrations among patches) were frequent at site A (28·8% of recaptured individuals had moved from one pond to another during the study), no such local migration was detected at site B, despite high movement rates. These results show that individuals in both populations had dispersal propensity but that they differed in the modalities of dispersal, particularly in the distances covered by a newt emigrating from a given breeding pond.

theoretical implications

High dispersal rates in adult newts can result in selection pressures from rapid habitat succession leading to positive relationships between dispersal and fitness in avoiding to stay at sites where there is no future (high extinction rates). The reasons why nomadic behaviour does not characterise the whole population may be because newts also benefit from site fidelity when breeding ponds are at those stages of ecological succession that provide valuable resources (food and egg-laying supports). Such site fidelity was demonstrated in several amphibians (Twitty 1959; Carpenter & Gillingham 1987; Sinsch 1992; Luddecke 1996) and in particular the Alpine newt (Joly & Miaud 1989b). This behaviour appears to be related to the temporal predictability of the breeding site (Tarkhnishvili 1994). In this way, site fidelity was modelled as an optimal strategy in cases of a predictable habitat and of previous reproductive success (Switzer 1993). If estimating reproductive success remains unfeasible in newts, the predictability and suitability of each breeding pond throughout the study suggests some benefit from site fidelity in these populations. The co-occurrence of two dispersal tactics within a population sets an evolutionary problem that we cannot fully encompass because of our poor knowledge of the ecological conditions that have shaped newt dispersal strategies in the past. Prior to human modifications of the landscapes, the stability of newt aquatic sites indeed probably differed from that which prevails in the present landscape.

Variation of dispersal between sites suggests a flexibility of behaviour, the causes of which being either genetically determined or environmentally induced. The fact that new populations are by definition founded by individuals that exhibit a colonist phenotype lead us to assume the possible role of genetic variation between populations. However, phenotypic plasticity is also suspected to be involved because new habitats differ from more mature ones in their prey density, availability of egg-laying supports, density and levels of relatedness. Although our data are not sufficient for exploring such hypotheses, our work provides insights about this crucial question of dispersal determinism. With regard to sex, we expected higher dispersal in males because their investment in reproduction is probably lower than that by females (there is an absence of male parental care, while females invest in both vitelline reserves and egg laying behaviour) (Greenwood 1980). The data disagree with this expectation as both sexes exhibit similar propensity for breeding dispersal. In contrast, available data show that propensity for natal dispersal is stronger in males than in females (Joly & Grolet 1996). Testable hypotheses could be that females invest in breeding dispersal in order to gain opportunities of sexual selection (conspecific attraction) or to increase their fitness through access to better resources such as egg-laying supports or food. Such high flexibility of female behaviour depends on the costs of dispersal for which we do not have any data.

Few empirical works have addressed the biology of dispersal because of the difficulties in identifying dispersers and of estimating parameters in theoretical models (Johnson & Gaines 1990; Ronce et al. 2001). In the present study, we attempted to circumvent this tricky problem by using a practical demographic tool. Very few studies have applied transient analysis (Pradel et al. 1995; Prévot-Julliard et al. 1998; Oro & Pradel 2000) and none were able to relate transience to behaviour. The implications of such studies are crucial for population biology and conservation biology where, for the latter, a lack of data and of reliable tools constitutes a gap between theory and practice. However, the significance of our results is tempered by the need for other studies. Further studies are needed to compare several newt population systems to allow for a rigorous testing of theoretical options.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

This paper benefited by the revisions of I. Olivieri, E. Pattee, N. Yoccoz and an anonymous referee. This work was supported by funding from the French Environment Ministry (n°91009). C.M. a grant from the French Ministry of Research and Technology. The collaboration between N.P. and R.P. was supported by a CNRS funding of the Réseau Populations Fragmentées. J. P. Léna helped in performing age structure analyses. We thank M. M. Curt and Chambard, owners of the pastures of site A, for their cooperation. The ponds of site B were created by Pierre Vérots Foundation in the natural estate it manages at Saint-Jean-de-Thurigneux (Ain, France). We are indebted to Benoît Castanier (PVF), Wojteck Maille and many students of the Claude Bernard University for their valuable help in the fieldwork.

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  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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