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Abstract Beyond the study of the mean, functional ecology lacks a concise characterization of trait variance patterns across spatiotemporal scales. Traits are measured in different ways, using different metrics, and at different spatial (and rarely temporal) scales. This study expands on previous research by applying a ubiquitous and widely used empirical model—Taylor's Power Law—to functional trait variance with the goal of identifying general patterns of trait variance scaling (the behavior of trait variance across scales). We compiled data on tree seedling communities monitored over 10 years across 213 2 m2plots and functional trait data from a subtropical forest in Puerto Rico. We examined trait‐based Taylor's Power Law at nested spatial and temporal scales. The scaling of variance with the mean was idiosyncratic across traits suggesting that the drivers of variation are likely to differ across traits that may make variance scaling theory elusive. However, slopes varied more in space than through time, suggesting that spatial environmental variability may have a larger role in driving trait variance than temporal variability. Empirical models that characterize taxonomic patterns across spatiotemporal scales, like Taylor's Power Law, can provide an insight into the scaling of functional traits, a necessary next step toward a more predictive trait‐based ecology.
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This content will become publicly available on December 1, 2025
Coefficients in Taylor's law increase with the time scale of water clarity measurements in a global suite of lakes
Abstract Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor's law of fluctuation scaling, which states that the variance of a population's size or density is proportional to a positive power of the mean size or density, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. Here, we use water clarity observations from 10,531 days of high‐frequency measurements in 35 globally distributed lakes, and lower‐frequency measurements over multiple decades from 6342 lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide the first documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor's law, and that model coefficients increase in a predictable yet non‐linear manner with decreasing observation frequency. This discovery effectively links high‐frequency sensor network observations with long‐term historical monitoring records, thereby affording new opportunities to understand and predict ecological dynamics on time scales from days to decades.
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- Award ID(s):
- 2217817
- PAR ID:
- 10586149
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- wiley
- Date Published:
- Journal Name:
- Ecology Letters
- Volume:
- 27
- Issue:
- 12
- ISSN:
- 1461-023X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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