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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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Analyzing multi-scale spatial point patterns in a pyramid modeling framework
Many spatial analysis methods suffer from the scaling issue identified as part of the Modifiable Areal Unit Problem (MAUP). This article introduces the Pyramid Model (PM), a hierarchical data framework integrating space and spatial scale in a 3D environment to support multi-scale analysis. The utility of the PM is tested in examining quadrat density and kernel density, which are commonly used measures of point patterns. The two metrics computed from a simulated point set with varying scaling parameters (i.e. quadrats and bandwidths) are represented in the PM. The PM permits examination of the variation of the density metrics computed at all different scales. 3D visualization techniques (e.g. volume display, isosurfaces, and slicing) allow users to observe nested relations between spatial patterns at different scales and understand the scaling issue and MAUP in spatial analysis. A tool with interactive controls is developed to support visual exploration of the internal patterns in the PM. In addition to the point pattern measures, the PM has potential in analyzing other spatial indices, such as spatial autocorrelation indicators, coefficients of regression analysis and accuracy measures of spatial models. The implementation of the PM further advances the development of a multi-scale framework for spatio-temporal analysis.
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- Award ID(s):
- 2102019
- PAR ID:
- 10321420
- Date Published:
- Journal Name:
- Cartography and Geographic Information Science
- ISSN:
- 1523-0406
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/15230406.2022.2048419
