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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 Agro├ęcologie, 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

Growth rates of seed banks with the four major crop types.Red polygons represent the growth rates of the weed species with the four crop types (WC = winter cereals, OR = oilseed rape, M = maize and SF = sunflower), as indicated by the bottom right polygon. For each species, the dashed polygon represents a growth rate equal to 1 and the centre corresponds to a growth rate equal to zero. Blue polygons represent the mean growth rates of the 18 species in each crop type. Species names are indicated by EPPO codes.
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pone.0139278.g004: Growth rates of seed banks with the four major crop types.Red polygons represent the growth rates of the weed species with the four crop types (WC = winter cereals, OR = oilseed rape, M = maize and SF = sunflower), as indicated by the bottom right polygon. For each species, the dashed polygon represents a growth rate equal to 1 and the centre corresponds to a growth rate equal to zero. Blue polygons represent the mean growth rates of the 18 species in each crop type. Species names are indicated by EPPO codes.

Mentions: There were high interspecific and intraspecific variations between LHT estimates and growth rates and none of the 18 species had similar LHT values in the four crop types (see Figs 3 and 4, built from S2 and S3 Figs). Of the 18 species, only Mercurialis annua L. had a positive asymptotic growth rate in all four crop types. Eight species had a positive growth rate in at least one crop type, while ten species had a negative growth rate in the four crop types. The highest number of species with a positive growth rate included winter cereals and sunflower (4). Conversely, around 90% of the species (17) showed negative growth rates with maize (Fig 4). The damping ratio (S5 Table) revealed that most of the species had a low convergence speed to the asymptotic growth rate, suggesting that these weed populations are not yet in the equilibrium state and exhibit long transient dynamics. This was expected due to the high disturbance regimes in arable fields. However, few species populations, mostly in winter cereals, seemed closer to the equilibrium (e.g., damping ratio of 256 for Solanum nigrum L. in winter cereals; S5 Table).


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)

Growth rates of seed banks with the four major crop types.Red polygons represent the growth rates of the weed species with the four crop types (WC = winter cereals, OR = oilseed rape, M = maize and SF = sunflower), as indicated by the bottom right polygon. For each species, the dashed polygon represents a growth rate equal to 1 and the centre corresponds to a growth rate equal to zero. Blue polygons represent the mean growth rates of the 18 species in each crop type. Species names are indicated by EPPO codes.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139278.g004: Growth rates of seed banks with the four major crop types.Red polygons represent the growth rates of the weed species with the four crop types (WC = winter cereals, OR = oilseed rape, M = maize and SF = sunflower), as indicated by the bottom right polygon. For each species, the dashed polygon represents a growth rate equal to 1 and the centre corresponds to a growth rate equal to zero. Blue polygons represent the mean growth rates of the 18 species in each crop type. Species names are indicated by EPPO codes.
Mentions: There were high interspecific and intraspecific variations between LHT estimates and growth rates and none of the 18 species had similar LHT values in the four crop types (see Figs 3 and 4, built from S2 and S3 Figs). Of the 18 species, only Mercurialis annua L. had a positive asymptotic growth rate in all four crop types. Eight species had a positive growth rate in at least one crop type, while ten species had a negative growth rate in the four crop types. The highest number of species with a positive growth rate included winter cereals and sunflower (4). Conversely, around 90% of the species (17) showed negative growth rates with maize (Fig 4). The damping ratio (S5 Table) revealed that most of the species had a low convergence speed to the asymptotic growth rate, suggesting that these weed populations are not yet in the equilibrium state and exhibit long transient dynamics. This was expected due to the high disturbance regimes in arable fields. However, few species populations, mostly in winter cereals, seemed closer to the equilibrium (e.g., damping ratio of 256 for Solanum nigrum L. in winter cereals; S5 Table).

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 Agro├ęcologie, 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