The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Quantifying the extinction vortex
Abstract
We developed a database of 10 wild vertebrate populations whose declines to extinction were monitored over at least 12 years. We quantitatively characterized the final declines of these well‐monitored populations and tested key theoretical predictions about the process of extinction, obtaining two primary results. First, we found evidence of logarithmic scaling of time‐to‐extinction as a function of population size for each of the 10 populations. Second, two lines of evidence suggested that these extinction‐bound populations collectively exhibited dynamics akin to those theoretically proposed to occur in extinction vortices. Specifically, retrospective analyses suggested that a population size of n individuals within a decade of extinction was somehow less valuable to persistence than the same population size was earlier. Likewise, both year‐to‐year rates of decline and year‐to‐year variability increased as the time‐to‐extinction decreased. Together, these results provide key empirical insights into extinction dynamics, an important topic that has received extensive theoretical attention.
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Hierarchical Linear Models: Applications and Data Analysis Methods.
Longford, N. T., Bryk, A. S., & Raudenbush, S. W. (1993) · Contemporary Sociology · doi:10.2307/2075823 · Cited by 18,724
IUCN Red List of Threatened Species
(2012) · Choice Reviews Online · doi:10.5860/choice.49-6872 · Cited by 12,728
Hierarchical Linear Models: Applications and Data Analysis Methods.
Goldstein, H., Bryk, A. S., & Raudenbush, S. W. (1993) · Journal of the American Statistical Association · doi:10.2307/2290750 · Cited by 3,321
IUCN Red List of threatened species
(2005) · Choice Reviews Online · doi:10.5860/choice.43-2185 · Cited by 3,051
Lande, R. (1993) · The American Naturalist · doi:10.1086/285580 · Cited by 2,143
Quantitative conservation biology
Morris, W. F., & Doak, D. F. (2002) · Cited by 1,746
The ecological detective: confronting models with data
(1997) · Choice Reviews Online · doi:10.5860/choice.35-1501 · Cited by 1,726
Stochastic Population Dynamics in Ecology and Conservation
Lande, R., Engen, S., & Saether, B.-E. (2003) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198525257.001.0001 · Cited by 1,611
Conservation biology: The science of scarcity and diversity
Simberloff, D. (1987) · Trends in Ecology & Evolution · doi:10.1016/0169-5347(87)90072-3 · Cited by 1,048
Extinction vulnerability in marine populations
Dulvy, N. K., Sadovy, Y., & Reynolds, J. D. (2003) · Fish and Fisheries · doi:10.1046/j.1467-2979.2003.00105.x · Cited by 1,029
Cited by
Synergies among extinction drivers under global change
BROOK, B., SODHI, N., & BRADSHAW, C. (2008) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2008.03.011 · Cited by 2,031
Landscapes that work for biodiversity and people
Kremen, C., & Merenlender, A. M. (2018) · Science · doi:10.1126/science.aau6020 · Cited by 1,125
Individual dispersal, landscape connectivity and ecological networks
Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M., & Turlure, C. (2013) · Biological Reviews · doi:10.1111/brv.12000 · Cited by 714
Integrating evolutionary dynamics into cancer therapy
Gatenby, R. A., & Brown, J. S. (2020) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-0411-1 · Cited by 227
State of the World's Amphibians
Catenazzi, A. (2015) · Annual Review of Environment and Resources · doi:10.1146/annurev-environ-102014-021358 · Cited by 211
Metadata
- Authors
- Fagan, W. F., & Holmes, E. E.
- Venue
- Ecology Letters
- Publisher
- Wiley
- Published
- 2005-11-09
- Volume
- 9
- Issue
- 1
- Pages
- 51-60
- ISSN
- 1461-023X (ISSN-L)
- 1461-0248
- OpenAlex
- W2122879786
- Cited by
- 406
- References
- 31
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.