In plant ecology, characterising colonisation and extinction in plant metapopulations is challenging due to the non-detectable seed bank that allows plants to emerge after several years of absence. In this study, we used a Hidden Markov Model to characterise seed dormancy, colonisation and germination solely from the presence-absence of standing flora. Applying the model to data from a long-term survey of 38 annual weeds across France, we identified three homogeneous functional groups: (1) species persisting preferentially through spatial colonisation, (2) species persisting preferentially through seed dormancy and (3) a mix of both strategies. These groups are consistent with existing ecological knowledge, demonstrating that ecologically meaningful parameters can be estimated from simple presence-absence observations. These results indicate that such studies could contribute to the design of weed management strategies. They also open the possibility of testing life-history theories such as the dormancy/colonisation trade-off in natura.
Many species have a dormant stage in their life cycle, including seeds for plants. The dormancy stage influences the species dynamics but is often undetectable. One way to include dormancy is to model it as a hidden dynamical state within a Markovian framework. Models within this framework have already been proposed but with different limitations: only presence/absence observations are modelled, the dormancy stage is limited to one year, or colonisation from neighbouring patches is not taken into account. We propose a hidden Markov model that describes the local and regional dynamics of a species that can undergo dormancy with a potentially infinite dormancy time. Populations are modelled with abundance classes. Our model considers the colonisation process as the indistinguishable influence of neighbour non-dormant population states on a dormant population state in a patch. It would be expected that parameter estimation, hidden state estimation and prediction of the next non-dormant populations would have an exponential computational time in terms of the number of patches. However, we demonstrate that estimation, hidden state estimation and prediction are all achievable in a linear computational time. Numerical experiments on simulated data show that the state of dormant populations can easily be retrieved, as well as the state of future non-dormant populations. Our framework provides a simple and efficient tool that could be further used to analyse and compare annual plants dynamics like weed species survival strategies in crop fields.
La persistance des populations de plantes à fleur repose sur la colonisation et la dormance, cette dernière étant difficile à estimer car la banque de graines est rarement observée. Nous présentons une modélisation par chaînes de Markov cachées couplées qui représente explicitement ces deux processus. Nous l’illustrons sur l’estimation des paramètres clés de la dynamique des plantes adventices.
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