Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1111/ecog.06833
Lizenz creative commons licence
Titel (primär) Spatial replication can best advance our understanding of population responses to climate
Autor Compagnoni, A.; Evers, S.; Knight, T.M.
Quelle Ecography
Erscheinungsjahr 2024
Department BZF; iDiv; SIE
Band/Volume 2024
Heft 1
Seite von e06833
Sprache englisch
Topic T5 Future Landscapes
Keywords climate vulnerability assessment; demography; power analysis; sample size; sampling design; space-for-time substitution
Abstract Understanding the responses of plant populations dynamics to climatic variability is frustrated by the need for long-term datasets. Here, we advocate for new studies that estimate the effects of climate by sampling replicate populations in locations with similar climate. We first use data analysis on spatial locations in the conterminous USA to assess how far apart spatial replicates should be from each other to minimize temporal correlations in climate. We find that on average spatial locations separated by 316 Km (SD = 149Km) have moderate (0.5) correlations in annual precipitation. Second, we use simulations to demonstrate that spatial replication can lead to substantial gains in the range of climates sampled during a given set of years so long as the climate correlations between the populations are at low to moderate levels. Third, we use simulations to quantify how many spatial replicates would be necessary to achieve the same statistical power of a single-population, long-term data set under different strengths and directions of spatial correlations in climate between spatial replicates. Our results indicate that spatial replication is an untapped opportunity to study the effects of climate on demography and to rapidly fill important knowledge gaps in the field of population ecology.
dauerhafte UFZ-Verlinkung
Compagnoni, A., Evers, S., Knight, T.M. (2024):
Spatial replication can best advance our understanding of population responses to climate
Ecography 2024 (1), e06833 10.1111/ecog.06833