Stochastic models that predict trout population density or biomass on a mesohabitat scale

Hydrobiologia ◽  
1996 ◽  
Vol 337 (1-3) ◽  
pp. 1-9 ◽  
Author(s):  
Philippe Baran ◽  
Sovan Lek ◽  
Marc Delacoste ◽  
Alain Belaud
Keyword(s):  
Population Density ◽  
Stochastic Models ◽  
Trout Population

Effect of Population Density on Gillnet Catchability in Four Allopatric Populations of Brown Trout (Salmo trutta)

1992 ◽  
Vol 49 (8) ◽  
pp. 1539-1545 ◽  
Author(s):  
Reidar Borgstrøm
Keyword(s):  
Population Density ◽  
Brown Trout ◽  
Salmo Trutta ◽  
High Mountain ◽  
Catch Per Unit Effort ◽  
Power Functions ◽  
Trout Population ◽  
Brown Trout Salmo Trutta ◽  
Management Implication ◽  
Brown Trout Population

The gillnet catchability of allopatric brown trout (Salmo trutta) populations was studied in four Norwegian high-mountain lakes, with large differences in population densities. The number of fish and the length composition of the populations were estimated by mark–recapture. The catchability coefficient and the catch per unit effort were described by power functions, and the catchability was inversely related to number of fish present. An important fisheries management implication is that brown trout populations with low densities are more vulnerable to gillnets than populations with high densities. Furthermore, the use of gillnet catches as an estimator of brown trout population density will be biased.

Speckled trout population parameters, habitat conditions and management strategies in lakes in Nova Scotia, Canada

2016 ◽  
Vol 48 (2) ◽  
pp. 189
Author(s):  
Amy Ryan ◽  
John L. MacMillan
Keyword(s):  
Nova Scotia ◽  
Population Density ◽  
Yellow Perch ◽  
Management Strategies ◽  
Fork Length ◽  
Small Population ◽  
Population Parameters ◽  
Habitat Conditions ◽  
Trout Population ◽  
Density And Biomass

A principal components analysis was conducted on the results of published reports and data sets from technical papers that include speckled trout Salvelinus fontinalis population estimates from 14 Nova Scotia lakes. The purpose of this paper was to identify the factors that influenced trout density and biomass. Population parameters, including mean fork length (cm), population density (n/ha), and population biomass (kg/ha), relative to variation in lake size, acidity, and competitor species were assessed and compared among lakes. Populations with small mean length and slow growth displayed larger fish density and biomass suggesting density-dependence. Acidic conditions potentially impact spawning potential and reduced recruitment that resulted in small population density and larger trout. The number of other fish species present in the lakes was used as an index of competition and had the greatest impact on trout density and biomass in Nova Scotia lakes. Yellow perch seemed to have the most impact of all the competitor species. In five lakes that contained yellow perch the mean trout population biomass was 0.19 kg-ha-1 (0.2,SD) compared to 4.5 kg-ha-1 (0.26, SD) in eight lakes that did not contain perch species. Lentic habitat conditions can greatly influence the potential success of different fisheries management strategies.

Stochastic models for learning.

1955 ◽  
Vol 39 (6) ◽  
pp. 464-464
Author(s):  
John E. Anderson
Keyword(s):  
Stochastic Models

Convergence of regional population density in Russia over 120 years

2020 ◽  
pp. 133-158
Author(s):  
K. A. Kholodilin ◽  
Y. I. Yanzhimaeva
Keyword(s):  
Population Density ◽  
Spatial Dependence ◽  
Population Distribution ◽  
Regional Level ◽  
Capital City ◽  
County Level ◽  
Spatial Effects ◽  
Russian Regions ◽  
Regional Population ◽  
Regional Population Density

A relative uniformity of population distribution on the territory of the country is of importance from socio-economic and strategic perspectives. It is especially important in the case of Russia with its densely populated West and underpopulated East. This paper considers changes in population density in Russian regions, which occurred between 1897 and 2017. It explores whether there was convergence in population density and what factors influenced it. For this purpose, it uses the data both at county and regional levels, which are brought to common borders for comparability purposes. Further, the models of unconditional and conditional β-convergence are estimated, taking into account the spatial dependence. The paper concludes that the population density equalization took place in 1897-2017 at the county level and in 1926—1970 at the regional level. In addition, the population density increase is shown to be influenced not only by spatial effects, but also by political and geographical factors such as climate, number of GULAG camps, and the distance from the capital city.

Sign in / Sign up

Export Citation Format

Share Document