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Evaluación del Riesgo de Extinción de una Hierba Perenne: Datos Demográficos Contra Registros Históricos
Author(s) -
Lindborg Regina,
Ehrlén Johan
Publication year - 2002
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1046/j.1523-1739.2002.00509.x
Subject(s) - extinction (optical mineralogy) , endangered species , geography , population , vital rates , population growth , ecology , demography , perennial plant , habitat , population size , threatened species , biology , paleontology , sociology
Abstract: Demographic information is frequently used to project the long‐term extinction risk of endangered species, but the limitations of this approach have not been extensively discussed. We examined demographic data for the endangered perennial herb Primula farinosa with matrix models to assess population growth rates and extinction risks. The data came from six populations in contrasting habitats followed over a 4‐year period. The results of these demographic models were compared to the results of experimental manipulations and to the actual change in occurrence of P. farinosa over a 70‐year period in different habitat types. According to demographic models, all managed populations had a projected negative population growth rate and experienced a high extinction risk in 100 years, whereas unmanaged populations had increasing population sizes. In contrast, experiments and historical records suggested that continuous grazing is positively correlated with population persistence. Our results thus show that demographic studies done during a transient phase of population growth after management cessation may not capture the long‐term changes. In such cases, projections of population growth rates may give misleading guidance for conservation. Short‐term demographic studies are in many cases unlikely to correctly assess the survival probability of a species. We therefore argue that complementary information, such as long‐term historical data or experimental manipulations of the environment, should be used whenever possible.