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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

Relationship between the relative germination rate and relative seed production per plant in each crop type.Symbols indicate the crop type (winter cereals = circle, oilseed rape = cross, maize = square, sunflower = triangle). Relative germination rates were not correlated with the relative seed production rates in winter cereals (Spearman correlation test, ρ = -0.39, P-value = 0.103), oilseed rape (ρ = -0.32, P-value = 0.185), maize (ρ = -0.76, P-value = 3.34e-4) and sunflower (ρ = -0.40, P-value = 0.094), but were negatively correlated in maize.
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pone.0139278.g005: Relationship between the relative germination rate and relative seed production per plant in each crop type.Symbols indicate the crop type (winter cereals = circle, oilseed rape = cross, maize = square, sunflower = triangle). Relative germination rates were not correlated with the relative seed production rates in winter cereals (Spearman correlation test, ρ = -0.39, P-value = 0.103), oilseed rape (ρ = -0.32, P-value = 0.185), maize (ρ = -0.76, P-value = 3.34e-4) and sunflower (ρ = -0.40, P-value = 0.094), but were negatively correlated in maize.

Mentions: All LHT values for a given species varied between crop types. The highest variations were observed for species seed production (φa) and germination rates (σa), which varied significantly between crop types, ranging from values close to 0 to a maximum seed production or germination rate depending on the crop type (Fig 3). Relative species germination rates were not correlated with relative seed production rates in winter cereals (Spearman correlation test, ρ = -0.39, P-value = 0.103), oilseed rape (ρ = -0.32, P-value = 0.185), and sunflower (ρ = -0.40, P-value = 0.094), but were negatively correlated in maize (ρ = -0.76, P-value = 3.34e-4) (Fig 5). Overall, no species had maximum values of all LHTs in all four crop types (Fig 3).


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)

Relationship between the relative germination rate and relative seed production per plant in each crop type.Symbols indicate the crop type (winter cereals = circle, oilseed rape = cross, maize = square, sunflower = triangle). Relative germination rates were not correlated with the relative seed production rates in winter cereals (Spearman correlation test, ρ = -0.39, P-value = 0.103), oilseed rape (ρ = -0.32, P-value = 0.185), maize (ρ = -0.76, P-value = 3.34e-4) and sunflower (ρ = -0.40, P-value = 0.094), but were negatively correlated in maize.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139278.g005: Relationship between the relative germination rate and relative seed production per plant in each crop type.Symbols indicate the crop type (winter cereals = circle, oilseed rape = cross, maize = square, sunflower = triangle). Relative germination rates were not correlated with the relative seed production rates in winter cereals (Spearman correlation test, ρ = -0.39, P-value = 0.103), oilseed rape (ρ = -0.32, P-value = 0.185), maize (ρ = -0.76, P-value = 3.34e-4) and sunflower (ρ = -0.40, P-value = 0.094), but were negatively correlated in maize.
Mentions: All LHT values for a given species varied between crop types. The highest variations were observed for species seed production (φa) and germination rates (σa), which varied significantly between crop types, ranging from values close to 0 to a maximum seed production or germination rate depending on the crop type (Fig 3). Relative species germination rates were not correlated with relative seed production rates in winter cereals (Spearman correlation test, ρ = -0.39, P-value = 0.103), oilseed rape (ρ = -0.32, P-value = 0.185), and sunflower (ρ = -0.40, P-value = 0.094), but were negatively correlated in maize (ρ = -0.76, P-value = 3.34e-4) (Fig 5). Overall, no species had maximum values of all LHTs in all four crop types (Fig 3).

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