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Ahnelt, H., & Tiefenbach, O. (1994). Verbreitungsmuster zweier Steinbeißerarten (Cobitis aurata, Cobitis taenia) im Einzugsgebiet der Mur (Österreich) (Vol. 7).
Schlüsselwörter: Fisch, Steinbeißer, Cobitis taenia, Cobitis aurata, Vorkommen, Verbreitung, Österreich
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Ahnelt, H., & Tiefenbach, O. (1991). Zum Auftreten des Blaubandbärblings (Pseudorasbora parva) (Teleostei: Gobioidae) in den Flüssen Raab und Lafnitz.
Schlüsselwörter: Fisch, Neozoon, Blaubandbärblings, Pseudorasbora parva, Vorkommen
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Ahnelt, H. (2004). Zwei Goldsteinbeißerarten (Teleostei, Cobitidae, Sabanejewia) in Österreich? (Vol. 57).
Schlüsselwörter: Fisch, Steinbeißer, Cobitis aurata, Sabanejewia aurata, Systematik
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Ahnelt, H., & Patzner, R. (1992). Über ein Vorkommen des Neunstachligen Stichlings (Pungitius pungitius, Teleostei: Gsterosteidae) in Österreich (Vol. 45).
Schlüsselwörter: Fisch, Neunstachliger Stichling, Pungitius pungitius, Vorkommen, Neozoon, Österreich
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Aigner, R. (1983). Der Flußkrebs und seine wirtschaftliche Bedeutung (Vol. 36).
Schlüsselwörter: Krebs, Astacus astacus, Flusskrebs
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Aigner, T. (1996). Entwässerungsgräben – Ersatzlebensräume für die heimische Kleinfischfauna? (Vol. 10).
Schlüsselwörter: Fisch, Graben, Vorkommen, Biotop
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Aitken, P. L. (1981). Dämme und Lachse in Schottland (Vol. 73).
Schlüsselwörter: Fisch, Salmo salar, Wanderung, Stau
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Akamine, T. (1989). An interval estimation for the Petersen method using Bayesian statistics.
Zusammenfassung: The statistical model for the Petersen method is a hypergeometric distribution. Approximation to a binomial distribution has been used, and the usual method for this binomial model is based on approximation to a normal distribution. The Bayesian statistical model for a binomial distribution, which assumes that the prior distribution of parameters is uniform, corresponds well with the conventional method. However, the Bayesian statistical method for a hypergeometric distribution which assumes the uniform prior distribution is not feasible. The prior distribution according to the inverse squared parameter is natural for this model. Beta function and zeta function are important to understand these methods. This model is simpler to understand and easier to calculate by micro-computer than the conventional method.
Schlüsselwörter: Bayesian, binominal, basic, listing, methode, theorie, algorithmus
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Akamine, T. (1989). An interval estimation for extraction using Bayesian statistics.
Zusammenfassung: The statistical model for extraction is a binomial distribution. The conventional method for employing this binomial model is based on approximation to a normal distribution. The Bayesian statistical method, which assumes that the prior distribution of parameters is uniform, is preferable to the conventional method, and two theorems demonstrate that this model corresponds well with the conventional method. Furthermore, this model is simpler to understand and easier to calculate by micro-computer than the conventional method.
Schlüsselwörter: Bayesian, normalverteilung, binominal, statistik, theorie, algorithmus, listing, basic
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Akamine, T. (1988). Estimation of parameter for Richards model.
Zusammenfassung: Akamine's (1986) BASIC program by Marquardt's method was rewritten for Richards model and its expanded model by the periodic function. For 0.9 similar to 1.1 the “LOG” function is corrected by Taylor series. Data estimated to be negative are cut off. AIC judges the effect of adding n to the parameters. Richards model is not so important in practice but it is important theoretically.
Schlüsselwörter: Wachstum, theorie, methode, statistik, listing, basic, modell
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