Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1111/ecog.06833
Licence creative commons licence
Title (Primary) Spatial replication can best advance our understanding of population responses to climate
Author Compagnoni, A.; Evers, S.; Knight, T.M.
Source Titel Ecography
Year 2024
Department BZF; iDiv; SIE
Volume 2024
Issue 1
Page From e06833
Language englisch
Topic T5 Future Landscapes
Data and Software links
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.
Persistent UFZ Identifier
Compagnoni, A., Evers, S., Knight, T.M. (2024):
Spatial replication can best advance our understanding of population responses to climate
Ecography 2024 (1), e06833