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Dynamics of Weeds in the Soil Seed Bank: A Hidden Markov Model to Estimate Life History Traits from Standing Plant Time Series.

Borgy B, Reboud X, Peyrard N, Sabbadin R, Gaba S - PLoS ONE (2015)

Bottom Line: However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes.Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values.There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal).

View Article: PubMed Central - PubMed

Affiliation: INRA, UMR1347 Agroécologie, Dijon, France; Centre National de Recherche Scientifique, Centre d'Ecologie Fonctionnelle et Evolutive, UMR 5175, Montpellier, France.

ABSTRACT
Predicting the population dynamics of annual plants is a challenge due to their hidden seed banks in the field. However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes. In agroecosystems, most weed seeds survive during unfavourable seasons and persist for several years in the seed bank. This causes difficulties in making accurate predictions of weed population dynamics and life history traits (LHT). Consequently, it is very difficult to identify management strategies that limit both weed populations and species diversity. In this article, we present a method of assessing weed population dynamics from both standing plant time series data and an unknown seed bank. We use a Hidden Markov Model (HMM) to obtain estimates of over 3,080 botanical records for three major LHT: seed survival in the soil, plant establishment (including post-emergence mortality), and seed production of 18 common weed species. Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values. The results showed that the LHT provided by the HMM enabled fairly accurate estimates of weed populations in different crops. There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal). The relationships between estimated LHTs and that between the estimated LHTs and the ecological characteristics of weeds provided insights into weed strategies. For example, a common strategy to cope with agricultural practices in several weeds was to produce less seeds and increase germination rates. This knowledge, especially of LHT for each type of crop, should provide valuable information for developing sustainable weed management strategies.

No MeSH data available.


Related in: MedlinePlus

Hidden Markov Model for abundance classes.cxt+1, the abundance class of the emerged plants at time t+1 depends on the abundance class of the weed population in the seed bank (cyt) and the management actions at at time t. At time t+1, the abundance class of the weed population in the seed bank cyt+1 is the sum of the output of the interaction between the abundance class of the weed population in the seed bank (cyt) and management actions at at time t and of the number of seeds produced by the emerged plants of this weed population at t+1 (cxt+1). The three LHTs are the germination rate σ, the seed survival rate in the seed bank s, and the seed production number φ, i.e., the number of seeds from each emerged plant in the seed bank. The values of these three LHTs (s, σ and φ) depend on the management action at.
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pone.0139278.g001: Hidden Markov Model for abundance classes.cxt+1, the abundance class of the emerged plants at time t+1 depends on the abundance class of the weed population in the seed bank (cyt) and the management actions at at time t. At time t+1, the abundance class of the weed population in the seed bank cyt+1 is the sum of the output of the interaction between the abundance class of the weed population in the seed bank (cyt) and management actions at at time t and of the number of seeds produced by the emerged plants of this weed population at t+1 (cxt+1). The three LHTs are the germination rate σ, the seed survival rate in the seed bank s, and the seed production number φ, i.e., the number of seeds from each emerged plant in the seed bank. The values of these three LHTs (s, σ and φ) depend on the management action at.

Mentions: The HMM was used to model the dynamics of each weed species. (cx1, cx2, …, cxT) are the observed variables corresponding to a time series of abundance classes of emerged weeds (cxt belongs to {1,2,3,4}) and T is the time series length. The hidden variables correspond to the time series of abundance classes in the seed bank: (cy1, cy2, …, cyT) with cyt taking value in {1,2,3,4,5,6}. The temporal relationship between these variables is described by two conditional probabilities (Fig 1):


Dynamics of Weeds in the Soil Seed Bank: A Hidden Markov Model to Estimate Life History Traits from Standing Plant Time Series.

Borgy B, Reboud X, Peyrard N, Sabbadin R, Gaba S - PLoS ONE (2015)

Hidden Markov Model for abundance classes.cxt+1, the abundance class of the emerged plants at time t+1 depends on the abundance class of the weed population in the seed bank (cyt) and the management actions at at time t. At time t+1, the abundance class of the weed population in the seed bank cyt+1 is the sum of the output of the interaction between the abundance class of the weed population in the seed bank (cyt) and management actions at at time t and of the number of seeds produced by the emerged plants of this weed population at t+1 (cxt+1). The three LHTs are the germination rate σ, the seed survival rate in the seed bank s, and the seed production number φ, i.e., the number of seeds from each emerged plant in the seed bank. The values of these three LHTs (s, σ and φ) depend on the management action at.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4591344&req=5

pone.0139278.g001: Hidden Markov Model for abundance classes.cxt+1, the abundance class of the emerged plants at time t+1 depends on the abundance class of the weed population in the seed bank (cyt) and the management actions at at time t. At time t+1, the abundance class of the weed population in the seed bank cyt+1 is the sum of the output of the interaction between the abundance class of the weed population in the seed bank (cyt) and management actions at at time t and of the number of seeds produced by the emerged plants of this weed population at t+1 (cxt+1). The three LHTs are the germination rate σ, the seed survival rate in the seed bank s, and the seed production number φ, i.e., the number of seeds from each emerged plant in the seed bank. The values of these three LHTs (s, σ and φ) depend on the management action at.
Mentions: The HMM was used to model the dynamics of each weed species. (cx1, cx2, …, cxT) are the observed variables corresponding to a time series of abundance classes of emerged weeds (cxt belongs to {1,2,3,4}) and T is the time series length. The hidden variables correspond to the time series of abundance classes in the seed bank: (cy1, cy2, …, cyT) with cyt taking value in {1,2,3,4,5,6}. The temporal relationship between these variables is described by two conditional probabilities (Fig 1):

Bottom Line: However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes.Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values.There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal).

View Article: PubMed Central - PubMed

Affiliation: INRA, UMR1347 Agroécologie, Dijon, France; Centre National de Recherche Scientifique, Centre d'Ecologie Fonctionnelle et Evolutive, UMR 5175, Montpellier, France.

ABSTRACT
Predicting the population dynamics of annual plants is a challenge due to their hidden seed banks in the field. However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes. In agroecosystems, most weed seeds survive during unfavourable seasons and persist for several years in the seed bank. This causes difficulties in making accurate predictions of weed population dynamics and life history traits (LHT). Consequently, it is very difficult to identify management strategies that limit both weed populations and species diversity. In this article, we present a method of assessing weed population dynamics from both standing plant time series data and an unknown seed bank. We use a Hidden Markov Model (HMM) to obtain estimates of over 3,080 botanical records for three major LHT: seed survival in the soil, plant establishment (including post-emergence mortality), and seed production of 18 common weed species. Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values. The results showed that the LHT provided by the HMM enabled fairly accurate estimates of weed populations in different crops. There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal). The relationships between estimated LHTs and that between the estimated LHTs and the ecological characteristics of weeds provided insights into weed strategies. For example, a common strategy to cope with agricultural practices in several weeds was to produce less seeds and increase germination rates. This knowledge, especially of LHT for each type of crop, should provide valuable information for developing sustainable weed management strategies.

No MeSH data available.


Related in: MedlinePlus