Hierarchical modeling strengthens evidence for density dependence in observational time series of population dynamics

Ecology ◽  
2019 ◽  
Vol 101 (1) ◽  
Author(s):  
Loïc M. Thibaut ◽  
Sean R. Connolly
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Density Dependence ◽  
Hierarchical Modeling

Ecological change points: The strength of density dependence and the loss of history

2017 ◽  
Author(s):  
José M. Ponciano ◽  
Mark L. Taper ◽  
Brian Dennis
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Density Dependence ◽  
Change Point ◽  
Likelihood Estimation ◽  
Theory And Practice ◽  
Change Points ◽  
Ecological Change ◽  
Proposed Model ◽  
Change Point Model

AbstractChange points in the dynamics of animal abundances have extensively been recorded in historical time series records. Little attention has been paid to the theoretical dynamic consequences of such change-points. Here we propose a change-point model of stochastic population dynamics. This investigation embodies a shift of attention from the problem of detecting when a change will occur, to another non-trivial puzzle: using ecological theory to understand and predict the post-breakpoint behavior of the population dynamics. The proposed model and the explicit expressions derived here predict and quantify how density dependence modulates the influence of the pre-breakpoint parameters into the post-breakpoint dynamics. Time series transitioning from one stationary distribution to another contain information about where the process was before the change-point, where is it heading and how long it will take to transition, and here this information is explicitly stated. Importantly, our results provide a direct connection of the strength of density dependence with theoretical properties of dynamic systems, such as the concept of resilience. Finally, we illustrate how to harness such information through maximum likelihood estimation for state-space models, and test the model robustness to widely different forms of compensatory dynamics. The model can be used to estimate important quantities in the theory and practice of population recovery.

Density dependence in a seasonal time series of the bamboo mosquito,Tripteroides bambusa(Diptera: Culicidae)

2017 ◽  
Vol 149 (3) ◽  
pp. 338-344 ◽  
Author(s):  
Tomonori Hoshi ◽  
Nozomi Imanishi ◽  
Kazuhiko Moji ◽  
Luis Fernando Chaves
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Density Dependence ◽  
Mosquito Species ◽  
Open Data ◽  
Parameter Estimates ◽  
Mosquito Abundance ◽  
Ricker Model ◽  
Forested Landscapes ◽  
Tripteroides Bambusa

AbstractThe bamboo mosquito,Tripteroides bambusa(Yamada) (Diptera: Culicidae), is a mosquito species ubiquitous across forested landscapes in Japan. During 2014 we sampled adult mosquitoes from May to November using a sweep net in Nagasaki, Japan. We recorded and managed our field data using Open Data Kit, which eased the overall process of data management before performing their statistical analysis. Here, we analyse the resulting biweekly time series of the bamboo mosquito abundance using time-series statistical techniques. Specifically, we test for density dependence in the population dynamics fitting the Ricker model. Parameter estimates for the Ricker model suggest that the bamboo mosquito is under density dependence regulation and that its population dynamics is stable. Our data also suggest the bamboo mosquito increased its abundance when temperature was more variable at our study site. Further work is warranted to better understand the linkage between the observed density dependence in the adults and the larvae of this mosquito species.

scholarly journals Immature survival, fertility, and density dependence drive global population dynamics in a long‐lived species

Ecology ◽  
2018 ◽  
Vol 99 (12) ◽  
pp. 2823-2832 ◽  
Author(s):  
M. Genovart ◽  
D. Oro ◽  
S. Tenan
Keyword(s):  
Population Dynamics ◽  
Density Dependence ◽  
Global Population ◽  
Immature Survival

scholarly journals Predictions of Taylor's power law, density dependence and pink noise from a neutrally modeled time series

2010 ◽  
Vol 265 (1) ◽  
pp. 78-86 ◽  
Author(s):  
Petr Keil ◽  
Tomáš Herben ◽  
James Rosindell ◽  
David Storch
Keyword(s):  
Time Series ◽  
Density Dependence ◽  
Power Law ◽  
Pink Noise ◽  
Taylor’S Power Law ◽  
Taylor's Power Law

scholarly journals Effects of temporal variation in temperature and density dependence on insect population dynamics

Ecosphere ◽  
2016 ◽  
Vol 7 (5) ◽  
Author(s):  
C. V. Haridas ◽  
Lance J. Meinke ◽  
Bruce E. Hibbard ◽  
Blair D. Siegfried ◽  
Brigitte Tenhumberg
Keyword(s):  
Population Dynamics ◽  
Temporal Variation ◽  
Density Dependence ◽  
Insect Population ◽  
Insect Population Dynamics

scholarly journals The Effect of Conspecific Density on Emergence ofLestes bipupillatusCalvert, 1909 (Odonata: Lestidae)

2014 ◽  
Vol 2014 ◽  
pp. 1-3 ◽  
Author(s):  
Ricardo Cardoso-Leite ◽  
Gabriel C. Vilardi ◽  
Rhainer Guillermo-Ferreira ◽  
Pitágoras C. Bispo
Keyword(s):  
Population Dynamics ◽  
Density Dependence ◽  
Social Environment ◽  
Growth Rates ◽  
High Density ◽  
Conspecific Density ◽  
Simulated Environments ◽  
The Social ◽  
Emergence Date

Conspecific density may influence adult recruitment and consequently population dynamics. Several studies have shown the density dependence of larvae growth rates in Odonata. However, few studies studied how conspecific density influence final instar larvae emergence date decisions. Considering that larvae may choose the date of emergence, the present study investigated if density affects larvae choice. For this, we reared eight final instar larvae in individual aquaria and other 24 larvae in aquaria with three larvae each. This way, we simulated environments with low and high larval densities. We then noted the days that larvae took to emerge and compared it between low and high density groups. The results showed that larvae seem to emerge earlier when in high densities (Mann-Whitney,U=10.000,P=0.03). These results support the hypothesis that damselfly last instar larvae may postpone or hasten emergence in response to the social environment and related constraints.

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