| Title: |
Density, Climate, and Stochasticity Shape Four Centuries of Population Dynamics for Two Long‐Lived Tree Species. |
| Authors: |
Waddle, Ellen1 (AUTHOR) ellen.waddle@colorado.edu; Lesser, Mark R.2 (AUTHOR); Steenbock, Christopher1 (AUTHOR); Doak, Daniel F.3 (AUTHOR) |
| Source: |
Ecology & Evolution (20457758). Dec2024, Vol. 14 Issue 12, p1-15. 15p. |
| Subject Terms: |
*Population dynamics; *Endangered species; *Species; Ponderosa pine; Pine |
| Abstract: |
The dynamics of colonizing populations may be strongly influenced by both extrinsic (e.g., climate and competition) and intrinsic (e.g., density) forces as well as demographic and environmental stochasticity. Understanding the impacts of these effects is crucial for predicting range expansions, trailing edge dynamics, and the viability of rare species, but the general importance of each of these forces remains unclear. Here, we assemble establishment time and spatial locations of most individuals that have reached maturity in six isolated, establishing populations of two pine species. These data allow us to quantify the relative importance of multiple factors in controlling growth of these populations. We found that climate, density, site, and demographic stochasticity were of varying importance both within and across species, but that no driver appeared to dominate dynamics across all populations and time periods. Indeed, exclusion of any one of these effects greatly reduced predictive power of our population growth models. Given the similarity in the abiotic characteristics of these sites, the varying importance of these classes of effects was surprising but speaks to the need to consider multiple effects when predicting the dynamics of small and colonizing populations. [ABSTRACT FROM AUTHOR] |
| : |
Copyright of Ecology & Evolution (20457758) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
GreenFILE |