Population Level Consequences of Toxicological Influences on Individual Growth and Reproduction inLumbricus rubellus(Lumbricidae, Oligochaeta)

1996 ◽  
Vol 33 (2) ◽  
pp. 118-127 ◽  
Author(s):  
Chris Klok ◽  
André M. de Roos
Keyword(s):  
Population Level ◽  
Individual Growth ◽  
Growth And Reproduction

scholarly journals The regulating effect of growth plasticity on the dynamics of structured populations

2021 ◽  
Author(s):  
Jasper Croll ◽  
André M. de Roos
Keyword(s):  
Growth Rate ◽  
Population Cycles ◽  
Population Level ◽  
Structured Populations ◽  
Individual Growth ◽  
Structured Population Model ◽  
Consumer Population ◽  
Individual Growth Rate ◽  
The Individual ◽  
Growth And Reproduction

Abstract Plasticity is the extent to which life history processes such as growth and reproduction depend on the environment. Plasticity in individual growth varies widely between taxa. Nonetheless, little is known about the effect of plasticity in individual growth on the ecological dynamics of populations. In this article we analyse a physiologically structured population model of a consumer population in which the individual growth rate can be varied between entirely plastic to entirely non-plastic. We derive this population level model from a dynamic energy budget model to ensure an accurate energetic coupling between ingestion, somatic maintenance, growth, and reproduction within an individual. We show that the consumer population is either limited by adult fecundity or juvenile survival up to maturation, depending on the level of growth plasticity and the non-plastic individual growth rate. Under these two regimes we also find two different types of population cycles which again arise due to fluctuation in respectively juvenile growth rate or adult fecundity. In the end our model not only provides insight into the effects of growth plasticity on population dynamics, but also provides a link between the dynamics found in age- and size-structured models.

Individual Growth and Reproduction

2019 ◽  
pp. 38-56
Author(s):  
Ken H. Andersen
Keyword(s):  
Life History ◽  
Growth Model ◽  
Life Histories ◽  
Maximum Size ◽  
Individual Growth ◽  
Energy Budgets ◽  
Asymptotic Size ◽  
The Individual ◽  
Growth And Reproduction

This chapter develops descriptions of how individuals grow and reproduce. More specifically, the chapter seeks to determine the growth and reproduction rates from the consumption rate, by developing an energy budget of the individual as a function of size. To that end, the chapter addresses the question of how an individual makes use of the energy acquired from consumption. It sets up the energy budgets of individuals by formulating the growth model using so-called life-history invariants, which are parameters that do not vary systematically between species. While the formulation of the growth model in terms of life-history invariants is largely successful, there is in particular one parameter that is not invariant between life histories: the asymptotic size (maximum size) of individuals in the population. This parameter plays the role of a master trait that characterizes most of the variation between life histories.

Production at the Cohort and Population Levels

2020 ◽  
pp. 149-168
Author(s):  
Michael J. Fogarty ◽  
Jeremy S. Collie
Keyword(s):  
Population Ecology ◽  
Life Stage ◽  
Population Level ◽  
Fish Population ◽  
Individual Growth ◽  
Relative Importance ◽  
Compensatory Mechanisms ◽  
Fisheries Science ◽  
Dominant Component ◽  
Time Period

The dominant focus on production processes in fisheries science sets it apart from other areas of population ecology in which population numbers are the principal currency for analysis. This chapter extends consideration of individual growth and mortality rates provided in earlier chapters to broaden the context for understanding cohort and population processes. A cohort is a group of organisms born within a given time period (e.g. year). How a fish population will respond to harvesting requires not only accurate accounting of its effective reproductive output but an understanding of the relative importance of compensatory mechanisms operating at different points in the life cycle. Recruitment (the number in a cohort surviving to a specified life stage or age) emerges as a dominant component of production at the population level. A dominant theme in this chapter concerns population regulation as embodied in the recruitment process and the high variability in this process.

Growth and reproductive parameters of Kalliapseudes schubartii in the estuarine region of the Lagoa dos Patos (southern Brazil)

2003 ◽  
Vol 83 (5) ◽  
pp. 931-935 ◽  
Author(s):  
Duane B. Fonseca ◽  
Fernando D'Incao
Keyword(s):  
Reproductive Activity ◽  
Individual Growth ◽  
Von Bertalanffy ◽  
Reproductive Parameters ◽  
Southern Brazil ◽  
Relative Growth ◽  
Von Bertalanffy Model ◽  
Soft Bottoms ◽  
Estuarine Region ◽  
Growth And Reproduction

Kalliapseudes schubartii (Crustacea: Tanaidacea) is a tube dwelling invertebrate living in estuarine soft bottoms with distribution along the south-east and southern Brazilian and Uruguayan coasts. Individual growth, and reproduction were examined by taking samples for a year in the estuarine region of the Lagoa dos Patos (southern Brazil). The von Bertalanffy model described growth of K. schubartii (K=4.54 y−1, L∞=13.22 mm). Reproductive activity was observed in spring and summer. No relationship was observed between total length of females and brood size. Eggs, embryos, and mancas were often observed in a marsupium. Relative growth analysis showed two levels of allometry in the growth of chelipeds of males.

scholarly journals Growth, death, and resource competition in sessile organisms

2021 ◽  
Vol 118 (15) ◽  
pp. e2020424118
Author(s):  
Edward D. Lee ◽  
Christopher P. Kempes ◽  
Geoffrey B. West
Keyword(s):  
Evolutionary Dynamics ◽  
Resource Competition ◽  
Temporal Dynamics ◽  
Population Level ◽  
Individual Growth ◽  
Local Competition ◽  
Large Colony ◽  
Rates Of Growth ◽  
Sessile Organisms

Population-level scaling in ecological systems arises from individual growth and death with competitive constraints. We build on a minimal dynamical model of metabolic growth where the tension between individual growth and mortality determines population size distribution. We then separately include resource competition based on shared capture area. By varying rates of growth, death, and competitive attrition, we connect regular and random spatial patterns across sessile organisms from forests to ants, termites, and fairy circles. Then, we consider transient temporal dynamics in the context of asymmetric competition, such as canopy shading or large colony dominance, whose effects primarily weaken the smaller of two competitors. When such competition couples slow timescales of growth to fast competitive death, it generates population shocks and demographic oscillations similar to those observed in forest data. Our minimal quantitative theory unifies spatiotemporal patterns across sessile organisms through local competition mediated by the laws of metabolic growth, which in turn, are the result of long-term evolutionary dynamics.

scholarly journals Estimating flowering transition dates from status-based phenological observations: a test of methods

2019 ◽  
Author(s):  
Shawn D Taylor
Keyword(s):  
Monte Carlo ◽  
Weibull Distribution ◽  
Population Level ◽  
Monte Carlo Analysis ◽  
Sampling Frequency ◽  
Herbarium Specimens ◽  
Echinacea Angustifolia ◽  
Final Sample ◽  
Growth And Reproduction

The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of in-situ flowering observations of the perennial forb *Echinacea angustifolia*. In this dataset, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known to within 3 days. I used a Monte Carlo analysis to test eight different estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I evaluated the estimators accuracy in predicting the onset, peak, and end of flowering at the population level, and predicting onset and end of flowering for individual plants. Overall a method using a Weibull distribution performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise the Weibull method is adequate for individual estimates as well. These methods allow practitioners to effectively utilize the large amount of status-based phenological observations currently available.

scholarly journals Estimating flowering transition dates from status-based phenological observations: a test of methods

2019 ◽  
Author(s):  
Shawn D Taylor
Keyword(s):  
Monte Carlo ◽  
Weibull Distribution ◽  
Population Level ◽  
Monte Carlo Analysis ◽  
Sampling Frequency ◽  
Herbarium Specimens ◽  
Echinacea Angustifolia ◽  
Final Sample ◽  
Growth And Reproduction

The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of in-situ flowering observations of the perennial forb *Echinacea angustifolia*. In this dataset, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known to within 3 days. I used a Monte Carlo analysis to test eight different estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I evaluated the estimators accuracy in predicting the onset, peak, and end of flowering at the population level, and predicting onset and end of flowering for individual plants. Overall a method using a Weibull distribution performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise the Weibull method is adequate for individual estimates as well. These methods allow practitioners to effectively utilize the large amount of status-based phenological observations currently available.

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