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Realistic Choice of Annual Matrices Contracts the Range of λS Estimates” under Reproductive Uncertainty Tooстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
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Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 8 декабря 2021 г.
- Авторы: Logofet D.O., Golubyatnikov L.L., Kazantseva E.S., Ulanova N.G.
- Журнал: Mathematics
- Том: 9
- Номер: 23
- Год издания: 2021
- Издательство: MDPI
- Местоположение издательства: Basel, Switzerland
- Номер статьи: 3007
- DOI: 10.3390/math9233007
- Аннотация: Our study is devoted to a subject popular in the field of matrix population models, namely, estimating the stochastic growth rate, λS, a quantitative measure of long-term population viability, for a discrete-stage-structured population monitored during many years. “Reproductive uncertainty” refers to a feature inherent in the data and life cycle graph (LCG) when the LCG has more than one reproductive stage, but when the progeny cannot be associated to a parent stage in a unique way. Reproductive uncertainty complicates the procedure of λS estimation following the defining of λS from the limit of a sequence consisting of population projection matrices (PPMs) chosen randomly from a given set of annual PPMs. To construct a Markov chain that governs the choice of PPMs for a local population of Eritrichium caucasicum, an short-lived perennial alpine plant species, we have found a local weather index that is correlated with the variations in the annual PPMs, and we considered its long time series as a realization of the Markov chain that was to be constructed. Reproductive uncertainty has required a proper modification of how to restore the transition matrix from a long realization of the chain, and the restored matrix has been governing random choice in several series of Monte Carlo simulations of long-enough sequences. The resulting ranges of λS estimates turn out to be more narrow than those obtained by the popular i.i.d. methods of random choice (independent and identically distributed matrices); hence, we receive a more accurate and reliable forecast of population viability
- Добавил в систему: Уланова Нина Георгиевна
Прикрепленные файлы
| № | Имя | Описание | Имя файла | Размер | Добавлен |
|---|---|---|---|---|---|
| 1. | Полный текст | статья полностью | Logofet_et_al._Mathematics-09-03007.pdf | 5,6 МБ | 9 декабря 2021 [UlanovaNinaGeorgievna] |