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Structural forecasting of species persistence under changing environments
Author(s) -
Saavedra Serguei,
Medeiros Lucas P.,
AlAdwani Mohammad
Publication year - 2020
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.13582
Subject(s) - persistence (discontinuity) , probabilistic logic , ecology , stability (learning theory) , econometrics , statistical model , climate change , computer science , population , mathematics , machine learning , artificial intelligence , biology , engineering , demography , geotechnical engineering , sociology
The persistence of a species in a given place not only depends on its intrinsic capacity to consume and transform resources into offspring, but also on how changing environmental conditions affect its growth rate. However, the complexity of factors has typically taken us to choose between understanding and predicting the persistence of species. To tackle this limitation, we propose a probabilistic approach rooted on the statistical concepts of ensemble theory applied to statistical mechanics and on the mathematical concepts of structural stability applied to population dynamics models – what we call structural forecasting . We show how this new approach allows us to estimate a probability of persistence for single species in local communities; to understand and interpret this probability conditional on the information we have concerning a system; and to provide out‐of‐sample predictions of species persistence as good as the best experimental approaches without the need of extensive amounts of data.