Rodent population cycles: life history adjustments to age-specific dispersal strategies and intrinsic time lags

Oecologia ◽  
1984 ◽  
Vol 64 (1) ◽  
pp. 8-13 ◽  
Author(s):  
Douglas W. Morris
Keyword(s):  
Life History ◽  
Population Cycles ◽  
Time Lags ◽  
Rodent Population ◽  
Intrinsic Time

scholarly journals Rodent host population dynamics drive zoonotic Lyme Borreliosis and Orthohantavirus infections in humans in Northern Europe

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahdi Aminikhah ◽  
Jukka T. Forsman ◽  
Esa Koskela ◽  
Tapio Mappes ◽  
Jussi Sane ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Lyme Borreliosis ◽  
Bank Vole ◽  
Myodes Glareolus ◽  
Northern Europe ◽  
Time Lags ◽  
Rodent Population ◽  
Vole Cycles ◽  
Infection Dynamics ◽  
Abundance Fluctuations

AbstractZoonotic diseases, caused by pathogens transmitted between other vertebrate animals and humans, pose a major risk to human health. Rodents are important reservoir hosts for many zoonotic pathogens, and rodent population dynamics affect the infection dynamics of rodent-borne diseases, such as diseases caused by hantaviruses. However, the role of rodent population dynamics in determining the infection dynamics of rodent-associated tick-borne diseases, such as Lyme borreliosis (LB), caused by Borrelia burgdorferi sensu lato bacteria, have gained limited attention in Northern Europe, despite the multiannual abundance fluctuations, the so-called vole cycles, that characterise rodent population dynamics in the region. Here, we quantify the associations between rodent abundance and LB human cases and Puumala Orthohantavirus (PUUV) infections by using two time series (25-year and 9-year) in Finland. Both bank vole (Myodes glareolus) abundance as well as LB and PUUV infection incidence in humans showed approximately 3-year cycles. Without vector transmitted PUUV infections followed the bank vole host abundance fluctuations with two-month time lag, whereas tick-transmitted LB was associated with bank vole abundance ca. 12 and 24 months earlier. However, the strength of association between LB incidence and bank vole abundance ca. 12 months before varied over the study years. This study highlights that the human risk to acquire rodent-borne pathogens, as well as rodent-associated tick-borne pathogens is associated with the vole cycles in Northern Fennoscandia, yet with complex time lags.

scholarly journals Population cycles and outbreaks of small rodents: ten essential questions we still need to solve

Oecologia ◽  
2020 ◽  
Author(s):  
Harry P. Andreassen ◽  
Janne Sundell ◽  
Fraucke Ecke ◽  
Stefan Halle ◽  
Marko Haapakoski ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Large Amplitude ◽  
Driving Forces ◽  
Population Cycles ◽  
Small Rodent ◽  
Small Rodents ◽  
Rodent Population ◽  
The World ◽  
Essential Questions ◽  
Rodent Populations

AbstractMost small rodent populations in the world have fascinating population dynamics. In the northern hemisphere, voles and lemmings tend to show population cycles with regular fluctuations in numbers. In the southern hemisphere, small rodents tend to have large amplitude outbreaks with less regular intervals. In the light of vast research and debate over almost a century, we here discuss the driving forces of these different rodent population dynamics. We highlight ten questions directly related to the various characteristics of relevant populations and ecosystems that still need to be answered. This overview is not intended as a complete list of questions but rather focuses on the most important issues that are essential for understanding the generality of small rodent population dynamics.

PLANE WAVES IN SYSTEMS HAVING AN INTRINSIC TIME LAG

2002 ◽  
Vol 12 (01) ◽  
pp. 193-204 ◽  
Author(s):  
SATISH R. INAMDAR ◽  
I. A. KARIMI
Keyword(s):  
Time Lag ◽  
Plane Waves ◽  
Ecological Systems ◽  
Complete Analysis ◽  
Time Lags ◽  
Predator Prey ◽  
Predator Prey Model ◽  
Intrinsic Time ◽  
Space Dependence ◽  
Two Parameter

Time delays in many biochemical and ecological systems are intrinsic and can be approximately modeled as discrete. If diffusive forces are present in such systems and delayed inputs to the system also have an intrinsic space dependence, then we get a set of partial differential equations with a discrete lag. This paper presents a general, more improved and fairly complete analysis of plane waves in systems with intrinsic time lags. In this paper, we identify four types of critical points that can arise in such a system and consider simultaneous perturbations in a system parameter and the discrete time lag near such points. We develop two-parameter plane wave solutions using the reductive perturbation theory, discuss their stability and apply the results to an example predator–prey model. The presence of a time lag necessitates an iterative procedure for getting the desired solutions.

Effects of life history and reproduction on recruitment time lags in reintroductions of rare plants

2019 ◽  
Vol 33 (3) ◽  
pp. 601-611 ◽  
Author(s):  
Matthew A. Albrecht ◽  
Oyomoare L. Osazuwa‐Peters ◽  
Joyce Maschinski ◽  
Timothy J. Bell ◽  
Marlin L. Bowles ◽  
...  
Keyword(s):  
Life History ◽  
Rare Plants ◽  
Time Lags ◽  
Recruitment Time

scholarly journals Estimating density dependence in time–series of age–structured populations

2002 ◽  
Vol 357 (1425) ◽  
pp. 1179-1184 ◽  
Author(s):  
R. Lande ◽  
S. Engen ◽  
B.–E. Sæther
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Life History ◽  
Density Dependence ◽  
Population Size ◽  
Adult Population ◽  
Structured Populations ◽  
Total Density ◽  
Time Lags ◽  
Long Time

For a life history with age at maturity α, and stochasticity and density dependence in adult recruitment and mortality, we derive a linearized autoregressive equation with time–lags of from 1 to α years. Contrary to current interpretations, the coefficients for different time–lags in the autoregressive dynamics do not simply measure delayed density dependence, but also depend on life–history parameters. We define a new measure of total density dependence in a life history, D , as the negative elasticity of population growth rate per generation with respect to change in population size, D = –∂lnλ T /∂ln N , where λ is the asymptotic multiplicative growth rate per year, T is the generation time and N is adult population size. We show that D can be estimated from the sum of the autoregression coefficients. We estimated D in populations of six avian species for which life–history data and unusually long time–series of complete population censuses were available. Estimates of D were in the order of 1 or higher, indicating strong, statistically significant density dependence in four of the six species.

Delayed Density-Dependent Season Length Alone Can Lead to Rodent Population Cycles

2006 ◽  
Vol 167 (5) ◽  
pp. 695
Author(s):  
Smith ◽  
White ◽  
Lambin ◽  
Sherratt ◽  
Begon
Keyword(s):  
Population Cycles ◽  
Season Length ◽  
Density Dependent ◽  
Rodent Population

scholarly journals A fitness trade-off between seasons causes multigenerational cycles in phenotype and population size

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Gustavo S Betini ◽  
Andrew G McAdam ◽  
Cortland K Griswold ◽  
D Ryan Norris
Keyword(s):  
Population Dynamics ◽  
Life History ◽  
Body Size ◽  
Natural Selection ◽  
Density Dependence ◽  
Seasonal Changes ◽  
Population Cycles ◽  
Trade Off ◽  
Viability Selection ◽  
Trade Offs

Although seasonality is widespread and can cause fluctuations in the intensity and direction of natural selection, we have little information about the consequences of seasonal fitness trade-offs for population dynamics. Here we exposed populations of Drosophila melanogaster to repeated seasonal changes in resources across 58 generations and used experimental and mathematical approaches to investigate how viability selection on body size in the non-breeding season could affect demography. We show that opposing seasonal episodes of natural selection on body size interacted with both direct and delayed density dependence to cause populations to undergo predictable multigenerational density cycles. Our results provide evidence that seasonality can set the conditions for life-history trade-offs and density dependence, which can, in turn, interact to cause multigenerational population cycles.

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