Plotting biological data in various ways.

2021 ◽  
pp. 37-78
Author(s):  
Donald L. J. Quicke ◽  
Buntika A. Butcher ◽  
Rachel A. Kruft Welton
Keyword(s):  
Population Size ◽  
Rural Population ◽  
Biological Data ◽  
Statistical Testing ◽  
Line Graphs ◽  
Confidence Limits ◽  
Trematode Infection ◽  
Error Bars

Abstract This chapter introduces plotting line graphs, bar charts, pie charts, box and whisker plots. It will troubleshoot the main areas where you are likely to encounter problems. It will show how to create log plots, add legends, error bars, notches and confidence limits, and introduce confidence limits and statistical testing. Examples are given, including bryophytes up a mountain; relationship between rural population size and the potential remaining intact forest; dietary differences between hornbill species (Buceros bicornis, Rhyticeros undulatus, Anthracoceros albirostris and Anorrhinus (Ptilolaemus) tickelli); and study of the level of trematode infection in various species of fish in Thailand.

Plotting biological data in various ways.

2021 ◽  
pp. 37-78
Author(s):  
Donald L. J. Quicke ◽  
Buntika A. Butcher ◽  
Rachel A. Kruft Welton
Keyword(s):  
Population Size ◽  
Rural Population ◽  
Biological Data ◽  
Statistical Testing ◽  
Line Graphs ◽  
Confidence Limits ◽  
Trematode Infection ◽  
Error Bars

Abstract This chapter introduces plotting line graphs, bar charts, pie charts, box and whisker plots. It will troubleshoot the main areas where you are likely to encounter problems. It will show how to create log plots, add legends, error bars, notches and confidence limits, and introduce confidence limits and statistical testing. Examples are given, including bryophytes up a mountain; relationship between rural population size and the potential remaining intact forest; dietary differences between hornbill species (Buceros bicornis, Rhyticeros undulatus, Anthracoceros albirostris and Anorrhinus (Ptilolaemus) tickelli); and study of the level of trematode infection in various species of fish in Thailand.

SECTORAL SPECIAL FEATURES OF TERRITORIAL DEVELOPMENT OF PETERSBURG CITY AGGLOMERATION

2021 ◽  
Vol 2 (65) ◽  
pp. 101-112
Author(s):  
Victor V. Solodilov ◽  
Keyword(s):  
Population Size ◽  
Rural Population ◽  
Territorial Development ◽  
Present Stage ◽  
Structural Units ◽  
Core Zone

The article presents the structure of the St. Petersburg city agglomeration at the present stage of its development: agglomeration core, zone of satellites, planning sectors. The problems and trends, sectoral special features of agglomeration development are described. The author describes the parameters of territorial development of the St. Petersburg city agglomeration, the main characteristics of its structural units: the population size, its density, the proportion of urban and rural population.

The Estimation of Population Size by a Marking and Recapture Procedure

1958 ◽  
Vol 15 (1) ◽  
pp. 19-25 ◽  
Author(s):  
D. B. DeLury
Keyword(s):  
Population Size ◽  
Confidence Limits ◽  
Regression Theory ◽  
Standard Regression

The Schumacher and Schnabel estimates of population size are compared and reasons are given for preferring the Schumacher formula. This formula is extended to permit mortality and recruitment in the population. Confidence limits are provided according to standard regression theory.

scholarly journals On Classes of Equivalence and Identifiability of Age-Dependent Branching Processes

2014 ◽  
Vol 46 (3) ◽  
pp. 704-718
Author(s):  
Rui Chen ◽  
Ollivier Hyrien
Keyword(s):  
Population Size ◽  
Markov Processes ◽  
Branching Processes ◽  
Biological Data ◽  
Equivalence Classes ◽  
Statistical Procedures ◽  
Age Dependent ◽  
Parametric Families ◽  
Dependent Processes

Age-dependent branching processes are increasingly used in analyses of biological data. Despite being central to most statistical procedures, the identifiability of these models has not been studied. In this paper we partition a family of age-dependent branching processes into equivalence classes over which the distribution of the population size remains identical. This result can be used to study identifiability of the offspring and lifespan distributions for parametric families of branching processes. For example, we identify classes of Markov processes that are not identifiable. We show that age-dependent processes with (nonexponential) gamma-distributed lifespans are identifiable and that Smith-Martin processes are not always identifiable.

scholarly journals Mathematical modeling of the hierarchical structure of the spatial distribution of the medieval rural population

2020 ◽  
Author(s):  
S. Shpirko ◽  
Keyword(s):  
Mathematical Modeling ◽  
Spatial Distribution ◽  
Population Size ◽  
Rural Population ◽  
Variational Approach ◽  
The Hierarchical Structure ◽  
The Subject ◽  
High Degree ◽  
The Relationship ◽  
Density Population

The subject of paper is the mathematical modeling of the spatial distribution of the medieval rural population. On the basis of the variational approach, two models of the hierarchy of centers are being developed, allowing with a high degree of reliability to identify the factors of the development of the settlement structure and to describe quantitatively the relationship between its most important parameters, such as density, population size and area.

scholarly journals Rural population dependence on the level of agricultural development: panel data analysis of Leningrad oblast

POPULATION ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 90-102
Author(s):  
Yulia Nikulina ◽  
Tatiana Yurchenko ◽  
Vladimir Surovtsev
Keyword(s):  
Panel Data ◽  
Rural Development ◽  
Population Size ◽  
Rural Areas ◽  
Rural Population ◽  
Agricultural Production ◽  
Agricultural Development ◽  
Small Scale ◽  
Leningrad Oblast ◽  
Agricultural Producers

Rural development has been and remains a relevant government task. Dynamic structural and technological changes in the agricultural sector lead to the need of reassessing the mutual influence of the level of development of agricultural production and rural areas. The study deals with quantitative assessment of the dependence of rural population size as an integral indicator of socio-economic well-being of rural areas on selected factors and indicators that characterize the level of agricultural development, its sectoral specifics and the structure of agricultural producers. Empirical estimates were obtained from panel data of municipal districts in Leningrad oblast for 2012-2018. The greatest positive impact on the rural population size among the considered characteristics of agriculture is determined for the factor of sown areas that is associated with the specifics of agricultural sub-sectors, their different needs for such factors as land and labor, the development potential for small-scale farming. It was found that the concentration of agricultural production in the large commercial sector has a negative impact on the rural population size. This is explained by difference in employment dynamics and redistribution of resources between categories of agricultural producers. Modeling results showed that agrarian subsidies received by agricultural producers have a statistically insignificant impact on rural population that justifies the need to adjust the orientation and forms of agricultural state support to achieve a synergetic effect on rural development.

scholarly journals How many giraffes are there? A comparison of abundance estimators at Ongava Game Reserve, Namibia

2021 ◽  
Author(s):  
Christophe Bonenfant ◽  
Ken Stratford ◽  
Stephanie Periquet
Keyword(s):  
Population Size ◽  
Detection Probability ◽  
Wildlife Conservation ◽  
Camera Trap ◽  
Biological Data ◽  
Camera Traps ◽  
Population Abundance ◽  
Visit Frequency ◽  
Game Reserve ◽  
Capture Recapture

Camera-traps are a versatile and widely adopted tool to collect biological data in wildlife conservation and management. If estimating population abundance from camera-trap data is the primarily goal of many projects, what population estimator is suitable for such data needs to be investigated. We took advantage of a 21 days camera-trap monitoring on giraffes at Onvaga Game Reserve, Namibia to compare capture-recapture (CR), saturation curves and N-mixture estimators of population abundance. A marked variation in detection probability of giraffes was observed in time and between individuals. Giraffes were also less likely to be detected after they were seen at a waterhole with cameras (visit frequency of f = 0.25). We estimated population size to 119 giraffes with a Cv = 0.10 with the best CR estimator. All other estimators we a applied over-estimated population size by ca. -20 to >+80%, because they did not account for the main sources of heterogeneity in detection probability. We found that modelling choices was much less forgiving for N-mixture than CR estimators. Double counts were problematic for N-mixture models, challenging the use of raw counts at waterholes to monitor giraffes abundance.

Sign in / Sign up

Export Citation Format

Share Document