Bayesian salamanders: analysing the demography of an underground population of the European plethodontid Speleomantes strinatii with state-space modelling
- Jan Lindström^{1}Email author,
- Richard Reeve^{1} and
- Sebastiano Salvidio^{2}
https://doi.org/10.1186/1472-6785-10-4
© Lindström et al; licensee BioMed Central Ltd. 2010
Received: 23 July 2009
Accepted: 2 February 2010
Published: 2 February 2010
Abstract
Background
It has been suggested that Plethodontid salamanders are excellent candidates for indicating ecosystem health. However, detailed, long-term data sets of their populations are rare, limiting our understanding of the demographic processes underlying their population fluctuations. Here we present a demographic analysis based on a 1996 - 2008 data set on an underground population of Speleomantes strinatii (Aellen) in NW Italy. We utilised a Bayesian state-space approach allowing us to parameterise a stage-structured Lefkovitch model. We used all the available population data from annual temporary removal experiments to provide us with the baseline data on the numbers of juveniles, subadults and adult males and females present at any given time.
Results
Sampling the posterior chains of the converged state-space model gives us the likelihood distributions of the state-specific demographic rates and the associated uncertainty of these estimates. Analysing the resulting parameterised Lefkovitch matrices shows that the population growth is very close to 1, and that at population equilibrium we expect half of the individuals present to be adults of reproductive age which is what we also observe in the data. Elasticity analysis shows that adult survival is the key determinant for population growth.
Conclusion
This analysis demonstrates how an understanding of population demography can be gained from structured population data even in a case where following marked individuals over their whole lifespan is not practical.
Keywords
Background
The human population relies on ecosystem services for its maintenance and well-being, and these can only be produced by functioning ecosystems [e.g. [1, 2]]. It has been argued that conserving nature and thereby retaining ecosystem functions is often vastly more profitable in economic terms than converting it, for instance, to forestry or agriculture [3]. Effective ecosystem conservation requires noticing changes both initially before active management has taken place and later to assess whether an employed conservation strategy is adequate. It is not realistic to have monitoring programmes for everything so indicator species are often used to gauge the health of a larger ecosystem [4, 5]. Biological properties that make a species or a taxonomic group attractive for use as indicators of ecosystem health are, for instance, clear taxonomy, existing information on basic biology, some measurable correlations to ecosystem health, limited mobility, practicality of sampling and relatively low variability of population fluctuations [5].
Once an indicator species has been identified, it is then necessary to know whether its population is stable, increasing or decreasing [6–8]. Such assessments are often based on statistical analyses of long-term population trends [9–11]. However, a much more thorough understanding of the situation can be gained by a more detailed demographic analysis whenever the extant data allow, especially when relatively small populations are considered [12–14]. This is because they are more sensititive to demographic and environmental stochasticity than larger populations and it is therefore important to understand how changes in population structure translate to changes in population growth rate [8]. Individual differences, such as age, sex, and condition, can greatly affect their contribution to population growth and consequently the same population size can lead to different population trajectories [15, 16]. Understanding the evolutionary selection pressures operating in a species also requires understanding the demography [17].
It has been suggested that Plethodontid salamanders, which comprise about 70% of the living urodelan species, are a suitable group to use as an indicator of ecosystem health [18, 19]. However, long-term studies on them are scarce [20] and demographic information for them is mainly based on life table and time series analyses of few terrestrial species from the USA and Europe [for a recent review, see [21]]. In general, these studies suggest that limiting factors, such as moisture, food and retreat or nesting sites may contribute to density-dependent population regulation [21]. Time series analysis on a population of the NW Italian Speleomantes strinatii showed that the population trajectory is stable and that the population was probably fluctuating near the environmental carrying capacity [22].
As most of the long-term population data on salamanders are from a small number of species, mainly North American Plethodon and Desmognathus species [e.g. [23, 24]], and from a single species of the European genus Speleomantes [25, 26], our understanding of the population regulation and demography of terrestrial salamanders is rather limited. Here we present an analysis based on a long-term data set on an underground population of Speleomantes strinatii using state-space modelling. This approach allows us to take into account both the inherent stochasticity in the demographic processes, and the uncertainty in the parameter estimates while using all of the available data to inform the model fit [27]. We explicitly model the underlying stochastic process, using Markov chain Monte Carlo (MCMC) simulation in a Bayesian framework [28–30].
Results
Abundance estimates were obtained for each age class and sex group in all years (N = 13). Mean capture probabilities (CP) were relatively high ranging from 0.60 to 0.72, and there were no significant differences among groups (repeated measures ANOVA, F = 1.37, DF = 4, P = 0.335) or years (repeated measures ANOVA, F = 0.97, DF = 12, P = 0.492), as noted by Salvidio [31]. There was no relationship between mean snout vent length (SVL) and CP (Spearman's ρ = 0.10, n = 5, P = 0.87), but it may be noteworthy that the smallest age group (i.e. first year juveniles) had the lowest CP. Overall these results indicate that the abundance estimates were reliable and should be considered with relatively high confidence as CP ≥ 0.40 provide "...really good results in the typical removal study, having N of a few hundred and t = 3 to 6" [32:108, where t is the number of removal occasions].
[8]. These results indicate a stable population as the λ value was very close to 1 (median and 95% confidence interval: 0.95, 0.91, 0.99). At population equilibrium, we therefore expect half of the individuals among these three stage-classes to be reproductive adults. This agrees with the observed population structure, where the overall mean proportion of the modelled stage-classes (i.e. juveniles, subadults and adults) was 20%, 27% and 53%, respectively. The adult stage appears very important to population growth, having both the highest reproductive value (1.51) and the highest elasticity of all the matrix entries (0.42 for adult survival).
Discussion
Here we present the basic population demography of a population of the European plethodontid Speleomantes strinatii using a Bayesian state-space model built from long-term population data. Although not obtained through individual marking, abundance estimates of the different age and sex groups were obtained calculating specific capture probabilities, that, in the study population, were relatively high. Thus these basic demographic data can be considered reliable [32, 33].
We found that the population growth rate estimate based on the parameterised Lefkovitch matrix model is very close to 1 giving us confidence that the further calculations based on this matrix assuming stable population are reliable [8]. This was further corroborated by the good match between the predicted and observed population stage-structure. The model also predicted the observed population reasonably well (Fig. 2B). We can therefore be reasonably confident that the other measures derived from the parameterised Lefkovitch model (age-specific survival and fertility and the age-within-stage distribution) are also reliable.
As this sub-population is part of a relatively dense population inhabiting a larger area, there is no reason to rule out immigration and emigration between years but without data on this we simply have to combine these processes with the survival and fecundity parameters. Better data on immigration and emigration would help to further narrow posterior distributions of all the parameter estimates. We also assumed 50:50 sex-ratio for births, and that the female fecundity is not limited by availability of mates. This is likely to be the case, as the adult population sex ratio (males/(males + females)) studied over 12 years was slightly male biased, being on average 0.57 (bootstrap 95% CI 0.53-0.65). This is probably due to the females entering the reproductive population one year later than males [25].
Correlation coefficients between the parameter estimates in the posterior chains.
Parameter | j _{1} | j _{2} | p _{1} | s | p _{2} | a | f | p _{obs} |
---|---|---|---|---|---|---|---|---|
j _{1} | 1.00 | |||||||
j _{2} | -0.76 | 1.00 | ||||||
p _{1} | 0.30 | -0.43 | 1.00 | |||||
s | -0.35 | 0.32 | -0.87 | 1.00 | ||||
p _{2} | 0.11 | -0.08 | 0.43 | -0.55 | 1.00 | |||
a | -0.12 | 0.10 | -0.44 | 0.48 | -0.91 | 1.00 | ||
f | -0.20 | -0.01 | -0.01 | 0.02 | -0.02 | -0.05 | 1.00 | |
p _{ obs } | -0.05 | 0.08 | -0.15 | 0.16 | -0.17 | 0.13 | -0.13 | 1.00 |
Conclusion
The modelling approach presented here is potentially very valuable in situations where following marked individuals over their whole lifetime is not feasible. For instance, the method introduced by Ricklefs [[40]; for use in Plethodontids, see [41])] calculate survival on the assumption that population growth rate is 1, an assumption we do not have to make. It was also recently shown [42] that state-space modelling approach produces results in close agreement with those based on mark-recapture techniques, highlighting further the potential of this approach.
Methods
Study species
Speleomantes strinatii is a medium-sized (total length < 115 mm) plethodontid salamander endemic to S France and NW Italy. It is found from the sea level up to about 2000 m a.s.l. on humid rocky outcrops, in the forest talus and in caves [43]. The aquatic larval stage is lacking and, during winter, females lay about 10 large terrestrial eggs that are attended for several months until hatching [43]. There are no field data on the proportion of successfully hatching embryos, and the only available data are from captive bred individuals. According to Durand [44], females lay 6-14 eggs but only half of them develop completely as females eliminate unfertilized or infected eggs from the clutch. In the case of a video surveilled female observed during the entire brooding period, only two out of nine attended eggs produced viable offsprings (Oneto et al. unpublished data). In this species, recruitment is seasonal and three immature body size groups (i.e. newborns, yearlings and subadults) may be recognised as separate age classes [45]. Males become sexually active at a snout-vent length (SVL) of about 50 mm when a mental gland, lacking in females and juveniles, becomes evident. A previous study based on dissections [25], demonstrated that females begin yolking at a SVL of 58 mm, and are probably reproductive at an older age than males.
From a conservation point of view, the species appears widespread and it is not considered globally endangered [43]. However, populations living in Mediterranean karstic areas that tend to concentrate inside caves or other artifical underground habitats, at least during unfavourable external environmental conditions, are highly exposed to disturbance, predation and or collection, and can thus be considered locally threatened.
Data collection and field methods
The study site is an artificial cavity excavated during World War II near Busalla (province of Genova, NW Italy) and naturally colonised by salamanders [26]. Cretaceous marlstones constitute the geological substrate of the area and a true natural karstic system is lacking, thus artificial underground retreats constitute an extension of the natural superficial underground compartment available to salamanders. During dry summer periods salamanders concentrate inside these buffered habitats in which low temperatures and high moisture levels are present, while they are less active on the soil surface.
During July, from 1996 to 2008, the population abundance was estimated by a standardised temporary removal experiment [32], with three removal samples obtained every other day (i.e. the total sampling period was 96 hours). During sampling the population was considered demographically closed, because dispersion from and towards the underground cavity was prevented by high temperatures and dry weather conditions, typical of summer climate in the region.
Salamanders caught on the cavity walls were measured from the snout to the posterior end of the cloaca to the nearest mm (snout-vent length, SVL) and kept in ventilated plastic boxes inside the cavity. Sex was determined only in adults, as mature males possess a conspicuous mental gland that is absent in females and immatures of both sexes. In each year sample the population was well structured, as all age groups were present, indicating that there was no transient emigration, a phenomenon that may bias the estimates of population demographic parameters [46]. At the end of the experiment, all salamanders were released. To delimit body size components of each year sample correctly, the population structure was separated into first and second year juveniles, subadults and adults by means of FiSAT software [47]. This procedure gives more reliable results than using fixed SVL values, because the population structure shows year to year variations. Thus the number of different age class individuals was estimated separately with the generalized removal model M_{bh} of CAPTURE software that allows for heterogeneity in capture probabilities [32]. For further details on the methods and the study population, see [22, 31, 48].
Parameter estimation
where the parameters represent transitions between the different stages J_{2} (juvenile), S (subadults) and A (reproductive adults), from one year to the next (Fig. 3). Note that as we had data available for the annual number of juveniles born in the year of the census, we utilised this information to introduce a transitional stage J_{1} and wrote the fecundity contribution of the adults as j_{1}f, where f is the expected number of female offspring produced by a female per year and j_{1} is the probability that this newly produced offspring survives until the age of one year, and therefore completes the transition from newly born to juvenile, J_{2}. The parameters p_{1}, p_{2}, j_{1}, j_{2}, s and a were estimated in such a way that each year adults first reproduce (at rate f), then all individuals survive or die (with stage-dependent survival probabilities p_{j 1}, p_{j 2}, p_{ s }and p_{ a }), and finally individuals in stages J_{1}, J_{2} and S will either continue to the next stage or stay where they are (with transition probabilities 1, γ_{1} and γ_{2} respectively - i.e. all new juveniles in J_{1} transition immediately to J_{2} before the end of the year). See Equation 4 for a mathematical description of this process.
We used a Bayesian framework for estimating the parameters of this demographic model, fitting the models using Monte Carlo Markov Chain (MCMC) techniques with the aid of the WinBUGS (version 1.4.3) statistical programming environment [28]. As very little prior knowledge on these demographic parameters is available, we used uninformative priors, using continuous uniform distributions as a starting point for each parameter. These were limited to 0 and 1 for all the other parameters except the fecundity parameter f for which the upper limit was set to 2 (as exploratory analyses indicated much smaller values for it). Note that this does not reflect the number of eggs laid, typically 6-14 in this species [44], but rather the number of hatched young per female. We also modelled the observation process explicitly, with a constant observation probability across all age classes and time periods [31], and hence the observed number in any class as binomially distributed from the true population at that time, with the probability of observation being a parameter of the model which is estimated from the data.
The WinBUGS model was run for 20,000 iterations with 5 chains allowing a "burn-in period" for 5,000 iterations. Convergence of the parameter estimates was confirmed with the Gelman-Rubin convergence statistic provided by the WinBUGS software, and in all cases the Monte Carlo error was less than 5% of the sample standard deviation (max. 2.2%). The observed autocorrelation in samples disappeared by setting the "thinning" to 50. In the analyses, we used the last 5,000 observations of the converged posterior chains. Since there was strong correlation between some of the model parameters (Table 1), we took 1,000 samples from these posteriors to parameterise the matrix model. All the subsequent simulations were run using MatLab 7.5.0 R2007b.
combines the calculation of estimated next generation stage sizes with the observation process to assess the accuracy of the simulation.
Declarations
Acknowledgements
JL would like to thank Hawthorne Beyer, Dan Haydon and Douglas Kerlin for helpful discussions. Thanks are also due to the "Gruppo Speleologico A. Issel" for allowing accession to the experimental site. Salamander sampling was authorised by the Italian Ministry of Environment (DPN-2008-0008213, for 2008). RR is funded by BBSRC grant BB/E010326/1.
Authors’ Affiliations
References
- Karr JR: Biological integrity: a long neglected aspect of water resources management. Ecol Appl. 1991, 1: 66-84. 10.2307/1941848.View ArticleGoogle Scholar
- Carpenter SR, Mooney HA, Agard J, Capistrano D, DeFries RS, Díaz S, Dietz T, Duraiappah AK, Oteng-Yeboah A, Pereira HM, Perrings C, Reid WV, Sarukhan J, Scholes RJ, Whyte A: Science for managing ecosystem services: Beyond the millennium ecosystem assessment. Proc Natl Acad Sci USA. 2009, 106: 1305-1312. 10.1073/pnas.0808772106.PubMed CentralView ArticlePubMedGoogle Scholar
- Balmford A, Bruner A, Cooper P, Costanza R, Farber S, Green RE, Jenkins M, Jefferiss P, Jessamy V, Madden J, Munro K, Myers N, Naeem S, Paavola J, Rayment M, Rosendo S, Roughgarden J, Trumper K, Turner RK: Economic reasons for conserving wild nature. Science. 2002, 297: 950-953. 10.1126/science.1073947.View ArticlePubMedGoogle Scholar
- Simberloff D: Flagships, umbrellas, and keystones: is single-species management passé in the landscape era?. Biol Conserv. 1998, 83: 247-257. 10.1016/S0006-3207(97)00081-5.View ArticleGoogle Scholar
- Hilty J, Merenleder A: Faunal indicator taxa selection for monitoring ecosystem health. Biol Conserv. 2000, 92: 185-197. 10.1016/S0006-3207(99)00052-X.View ArticleGoogle Scholar
- Burgman MA, Ferson S, Akçakaya HR: Risk assessment in conservation biology. 1993, London: Chapman & HallGoogle Scholar
- Harwood J: Risk assessment and decision analysis in conservation. Biol Conserv. 2000, 95: 219-226. 10.1016/S0006-3207(00)00036-7.View ArticleGoogle Scholar
- Caswell H: Matrix population models: construction, analysis and interpretation. 2001, Sunderland MA: SinauerGoogle Scholar
- James FC, McCulloch CE, Wiedenfeld DA: New approaches to the analysis of population trends in land birds. Ecology. 1996, 77: 13-27. 10.2307/2265650.View ArticleGoogle Scholar
- Conrad KF, Woiwod IP, Parsons M, Fox M, Warren MS: Long-term population trends in widespread British moths. J Insect Conserv. 2004, 8: 119-136.View ArticleGoogle Scholar
- Stuart SN, Chanson JS, Cox NA, Young BE, Rodrigues ASL, Fischman DL, Waller RW: Status and trends of amphibian declines and extinctions worldwide. Science. 2004, 306: 1783-1786. 10.1126/science.1103538.View ArticlePubMedGoogle Scholar
- Holmes EE, Fritz LW, York AE, Sweeney K: Age-structured modeling reveals long-term declines in the natality of western Steller sea lions. Ecol Appl. 2007, 17: 2214-2232. 10.1890/07-0508.1.View ArticlePubMedGoogle Scholar
- Jenouvrier S, Caswell H, Barbraud C, Holland M, Strœve J, Weimerskirch H: Demographic models and IPCC climate projections predict the decline of an emperor penguin population. Proc Natl Acad Sci USA. 2009, 106: 1844-1847. 10.1073/pnas.0806638106.PubMed CentralView ArticlePubMedGoogle Scholar
- Ozgul A, Oli MK, Armitage KB, Blumstein DT, Van Vuren DH: Influence of local demography on asymptotic and transient dynamics of a yellow-bellied marmot metapopulation. Am Nat. 2009, 173: 517-530. 10.1086/597225.View ArticlePubMedGoogle Scholar
- Coulson T, Catchpole EA, Albon SD, Morgan BJT, Pemberton JM, Clutton-Brock TH, Crawley MJ, Grenfell BT: Age, sex, density, winter weather, and population crashes in Soay sheep. Science. 2001, 292: 1528-1531. 10.1126/science.292.5521.1528.View ArticlePubMedGoogle Scholar
- Altwegg R, Dummermuth S, Anholt BR, Flatt T: Winter weather affects asp viper (Vipera aspis) population dynamics through susceptible juveniles. Oikos. 2005, 110: 55-66. 10.1111/j.0030-1299.2001.13723.x.View ArticleGoogle Scholar
- Metcalf CJ, Pavard S: Why evolutionary biologists should be demographers. Trends Ecol Evol. 2007, 22: 205-212. 10.1016/j.tree.2006.12.001.View ArticlePubMedGoogle Scholar
- Welsh HH, Droege S: A case for using Plethodontid salamanders for monitoring biodiversity and ecosystem integrity of North American forests. Cons Biol. 2001, 15: 558-569. 10.1046/j.1523-1739.2001.015003558.x.View ArticleGoogle Scholar
- Davic RD, Welsh HH: On the ecological role of salamanders. Annu Rev Ecol Evol Syst. 2004, 35: 405-434. 10.1146/annurev.ecolsys.35.112202.130116.View ArticleGoogle Scholar
- Hyde EJ, Simons TR: Sampling Plethodontid salamanders: sources of variability. J Wildl Manage. 65: 624-632.Google Scholar
- Bruce RC: Intraguild interactions and population regulation in plethodontid salamanders. Herpetol Monogr. 2008, 22: 31-55. 10.1655/07-015.1.View ArticleGoogle Scholar
- Salvidio S: Population dynamics and regulation in the cave salamander Speleomantes strinatii. Naturwissenschaften. 2007, 94: 396-400. 10.1007/s00114-006-0202-2.View ArticlePubMedGoogle Scholar
- Tilley SG: Life histories and comparative demography of two salamander populations. Copeia. 1980, 4: 806-821. 10.2307/1444460.View ArticleGoogle Scholar
- Homyack JA, Haas CA: Long-term effects of experimental harvesting on abundance and reproductive demography of terrestrial salamanders. Biol Cons. 2009, 142: 110-121. 10.1016/j.biocon.2008.10.003.View ArticleGoogle Scholar
- Salvidio S: Life history of the European plethodontid salamander Speleomantes ambrosii. Herpetol J. 1993, 3: 55-59.Google Scholar
- Salvidio S, Lattes A, Tavano M, Melodia F, Pastorino MV: Ecology of a Speleomantes ambrosii population inhabiting an artificial tunnel. Amphib-Reptil. 1994, 15: 35-45. 10.1163/156853894X00533.View ArticleGoogle Scholar
- Clark JS, Bjørnstad O: Population time series: process variability, observation errors, missing values, lags, and hidden states. Ecology. 2004, 85: 3140-3150. 10.1890/03-0520.View ArticleGoogle Scholar
- Lunn DJ, Thomas A, Best N, Spiegelhalter D: WinBUGS -- A Bayesian framework: concepts, structure, and extensibility. Statist Comput. 2000, 10: 325-337. 10.1023/A:1008929526011.View ArticleGoogle Scholar
- de Valpine P, Hastings A: Fitting population models incorporating process noise and observation error. Ecol Monogr. 2002, 72: 57-76.View ArticleGoogle Scholar
- McCarthy MA: Bayesian methods for ecology. 2007, Cambridge: Cambridge University PressView ArticleGoogle Scholar
- Salvidio S: Temporal variation of adult sex ratio in apopulation of the terrestrial salamander Speleomantes strinatii. Herpetol J. 2008, 18: 66-68.Google Scholar
- White GC, Anderson DR, Burnham KP, Otis DL: Capture-recapture removal methods for sampling closed populations. 1982, Los Alamos, New Mexico: Los Alamos National LaboratoryGoogle Scholar
- Williams BK, Conroy MJ, Nichols JD: Analysis and Management of Animal Populations. 2001, San Diego CA: Academic PressGoogle Scholar
- Schmidt BR, Feldmann R, Schaub M: Demographic processes underlying population growth and decline in Salamandra salamandra. Cons Biol. 2005, 19: 1149-1156. 10.1111/j.1523-1739.2005.00164.x.View ArticleGoogle Scholar
- Benton TG, Grant A: Elasticity analysis as an important tool in evolutionary and population ecology. Trends Ecol Evol. 1999, 14: 467-471. 10.1016/S0169-5347(99)01724-3.View ArticlePubMedGoogle Scholar
- Grant A, Benton TG: Elasticity analysis for density-dependent populations in stochastic environments. Ecology. 2000, 81: 680-693.View ArticleGoogle Scholar
- van Tienderen PH: Life cycle trade-offs in matrix population models. Ecology. 1995, 76: 2482-2489. 10.2307/2265822.View ArticleGoogle Scholar
- Reid JM, Bignal EM, Bignal S, McCracken DI, Monaghan P: Identifying the demographic determinants of population growth rate: a case study of red-billed choughs Pyrrhocorax pyrrhocorax. J Anim Ecol. 2004, 73: 777-788. 10.1111/j.0021-8790.2004.00854.x.View ArticleGoogle Scholar
- Bolker BM: Ecological models and data in R. 2008, Princeton: Princeton University PressGoogle Scholar
- Ricklefs RE: Comparative demography of New World populations of thrushes (Turdus spp.). Ecol Monogr. 1997, 67: 23-43.View ArticleGoogle Scholar
- Welsh HH, Pope KL, Wheeler CA: Using multiple metrics to assess the effects of forest succession on population status: A comparative study of two terrestrial salamanders in the US Pacific Northwest. Biol Cons. 2008, 141: 1149-1160. 10.1016/j.biocon.2008.02.014.View ArticleGoogle Scholar
- Tavecchia G, Besbeas P, Coulson T, Morgan BJT, Clutton-Brock TH: Estimating population size and hidden demographic parameters with state-space modelling. Am Nat. 2009, 173: 722-733. 10.1086/598499.View ArticlePubMedGoogle Scholar
- Lanza B, Pastorelli C, Laghi P, Cimmaruta R: A review of systematics, taxonomy, genetics, biogeography and natural history of the genus Speleomantes Dubois, 1984 (Amphibia Caudata Plethodontidae). Atti Museo Civico Storia Naturale, Trieste. 2005, 52 (suppl): 5-135.Google Scholar
- Durand J-P: Fortpflanzung und Entwicklung von Hydromantes, dem Höhlenmolch. Aqua Terra. 1970, 7: 42-48.Google Scholar
- Salvidio S, Pastorino MV: Spatial segregation in the European plethodontid Speleomantes strinatii in relation to age and sex. Amphib-Reptil. 2002, 23: 505-510. 10.1163/15685380260462400.View ArticleGoogle Scholar
- Bailey L, Kendall WL, Church DR, Wilbur HM: Estimating survival and breeding probability for pond-breeding amphibians: a modified robust design. Ecology. 2004, 85: 2456-2466. 10.1890/03-0539.View ArticleGoogle Scholar
- Gayanilo FC, Sparre P, Pauly D: The FAO-ICLARM stock assessment tools (FiSAT) user's guide. 1995, Rome: FAO Computerised Information SeriesGoogle Scholar
- Salvidio S: Estimating abundance and biomass of a Speleomantes strinatii (Caudata: Plethodontid) population by temporary removal sampling. Amphibia-Reptilia. 1998, 19: 113-124. 10.1163/156853898X00412.View ArticleGoogle Scholar
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