Analytical Population Dynamics

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Create Alert. Share This Paper. Citations Publications citing this paper. Host-driven population dynamics in an herbivorous insect. Ayres , Matti Rousi , Peter W. Spatially structured population dynamics in feral oilseed rape. Michael J. Crawley , Susan L. From patterns to processes: phase and density dependencies in the Canadian lynx cycle. Nils Chr. Stenseth , W. The theta-logistic is unreliable for modelling most census data: Theta-logistic model is not robust Francis Clark , Barry W.

Brook , Steven Delean , H. The relative roles of density and climatic variation on population dynamics and fecundity rates in three contrasting ungulate species.

Moving forward in circles: challenges and opportunities in modelling population cycles. Population cycles emerging through multiple interaction types Naoya Mitani , Akihiko Mougi. References Publications referenced by this paper. Crawford , Daniel T. An Atlas of Spruce Budworm Defoliation. Hardy , M. Mainville , D. The role of birds in a spruce budworm outbreak in Maine. Understanding the population dynamics of plants is fundamental to our ability to manage and predict ecosystem response, especially in the light of human alteration of climate. Although pest control is one of the areas in which population dynamics theory has been applied to solve practical problems [6] , the links between population dynamics theory and model construction have been less emphasized in the management and control of weed populations and invasive weedy species [8].

Chris Legault, Ph.D., Supv., Research Fishery Biologist

Most management models on weed populations dynamics have emphasized the role of endogenous process, i. These models produce stable dynamics and form the basis for weed management recommendations, yet exclude the role of the exogenous variables, i. To our knowledge, there are no other studies attempting to understand how both feedback structure and exogenous factors interact in shaping the dynamics of weed populations and their management.

Here, we use one of the longest data set 22 years in plant populations on two annual weed species from a locality in Central Spain to determine the importance of endogenous inter-specific interactions and exogenous processes climate. We focus on diagnosis and modeling tools from population-dynamics theory to analyze these long-term data and to determine the role of the North Atlantic Oscillation NAO and local weather as exogenous factors influencing weed dynamics.

In particular, we use the Royama [1] classification of exogenous effects as an organized approach to evaluating the effect of climate on population dynamics. In this way, we can include logical explanations of the possible effects of climate on demographic rates in the population dynamics models and also use independent data for testing model predictions.

The experimental site has a north-Mediterranean climate, with mild and humid winters and dry-hot summers Average annual rainfall during the year study period was mm, with a maximum precipitation of mm and a minimum of mm. The average temperature was All crops were grown under no-tillage and minimum tillage practices, maintaining plant residues close to the soil surface. Herbicides were sprayed for control of dicotyledonous weeds.

No fertilizers or herbicides were applied in the legume rotation phase. Detailed information about the experiment is given in [12].

Journal of the Optical Society of America B

Weed population densities were sampled annually except in and The sampling times and procedures used to quantify weed population density varied slightly depending on the type of crop cereal or legume and the weed population density. In the wheat rotation phase, sampling took place from February to March. In the legume rotation phase sampling was slightly later March to April. Thereafter, 10 samples were taken in each plot except in when 20 samples were taken 10 along each of two transects.

The collected material was kept in plastic bags and transported to the laboratory, where individual species were identified and counted. The different sampling intensities among years were due to the different weed densities present. In this paper we consider two important species in cereal agro-ecosystems: Descurainia sophia L.

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Both species have winter annual life histories with persistent seed banks and are relatively common in winter cereal crops grown in semi-arid areas [13] , [14]. Population dynamics of weeds are the result of the combined effects of feedback structure ecological interactions within and between plant populations , limiting factors nutrient and water limitation , climatic influences rainfall, temperature and stochastic forces [15].

To understand how these factors may determine weed fluctuations, we model both system-intrinsic processes and exogenous influences as a general model based on the R -function [2].

The R -function represents the realized per capita population growth rates that synthesize the processes of individual survival and reproduction [2]. This model represents the basic feedback structure and integrates the stochastic and climate forces that drive population dynamics in nature. Our first step was to estimate the order of the dynamical processes in eqn. First-order negative feedback processes are the results of intra-population interactions which involves a single variable the density of population itself due to the intra-specific competition for limiting resources [1] — [3].

Second-order feedback processes are produced by mutual causal process between two populations consumer-resource; predator-prey; host-parasitoid , because two variables are now involved in the negative loop, it is known as a second-order dynamic process [1] — [3] and it had been demonstrated that this system can be reduced to a second-order or lagged equation for one of the two species involved [1] — [4].

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We write eqn. Where R , the realized per-capita rate of change, is calculated from the data,. For statistical convenience we assumed a log-linear relationship between R and lagged population density [1].

We used the residuals of this model plus the mean logarithm of density as the detrended time series. In order to test model predictions we make the time series of no-tillage and minimum tillage treatments comparable by subtracting or adding the differences between the means of the detrended time series. Population dynamics of weeds have been suggested to be the result of intra-population processes which cause a first-order feedback structure in plant populations [15]. To understand how these processes determine weed dynamics, we used a simple model of intra-specific competition, the exponential form of the discrete logistic model [2] , [16] , and we employed its generalized version; 3.

We can defining the above equation in terms of the R -function, i. This model represents the basic feedback structure determined by intra-population processes. Because in this model the three parameters R m , a and C have an explicit biological interpretation we can include climate perturbations in each parameter using the framework of Royama [1]. In this manner, we can build mechanistic hypotheses about the effects of climate on weed populations.

This can be expressed as: 5. Another kind of climate perturbation is when the equilibrium point of the population is influenced by the climate. This is the case when climate influences a limiting factor or resource water, light or nutrients. The correct model structure in this scenario is that the carrying capacity equilibrium point is affected by the rainfall. A previous study determined that the species D. A second-order logistic model can be represented as: 7. As in the equation 3, N t-d represents the lagged weed densities, r m is a positive constant representing the maximum finite reproduction rate, c is a constant representing competition and resource depletion, a indicates the effect of interference on each individual as density increase [1].

Similar to eqn. We fitted equations 4 and 8 using the nls library in the program R by means of nonlinear regression analyses [18]. In addition, we included the climate variables in the parameters R m , C and a as linear functions eqs.

ISBN 10: 041275570X

Models with lowest AIC c values were selected.

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