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Ability of Matrix Models to Explain the Past and Predict the Future of Plant Populations

Overview of attention for article published in Conservation Biology, April 2013
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  • Above-average Attention Score compared to outputs of the same age (58th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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6 tweeters

Citations

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77 Dimensions

Readers on

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194 Mendeley
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Title
Ability of Matrix Models to Explain the Past and Predict the Future of Plant Populations
Published in
Conservation Biology, April 2013
DOI 10.1111/cobi.12049
Pubmed ID
Authors

ELIZABETH E. CRONE, MARTHA M. ELLIS, WILLIAM F. MORRIS, AMANDA STANLEY, TIMOTHY BELL, PAULETTE BIERZYCHUDEK, JOHAN EHRLÉN, THOMAS N. KAYE, TIFFANY M. KNIGHT, PETER LESICA, GERARD OOSTERMEIJER, PEDRO F. QUINTANA-ASCENCIO, TAMARA TICKTIN, TERESA VALVERDE, JENNIFER L. WILLIAMS, DANIEL F. DOAK, RENGAIAN GANESAN, KATHYRN MCEACHERN, ANDREA S. THORPE, ERIC S. MENGES

Abstract

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 194 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 4%
Brazil 4 2%
Spain 2 1%
Australia 1 <1%
France 1 <1%
New Zealand 1 <1%
Netherlands 1 <1%
Switzerland 1 <1%
Unknown 176 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 18%
Student > Ph. D. Student 34 18%
Student > Master 22 11%
Student > Bachelor 20 10%
Student > Doctoral Student 20 10%
Other 50 26%
Unknown 13 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 110 57%
Environmental Science 52 27%
Medicine and Dentistry 3 2%
Earth and Planetary Sciences 3 2%
Unspecified 2 1%
Other 6 3%
Unknown 18 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 February 2019.
All research outputs
#4,725,265
of 15,156,856 outputs
Outputs from Conservation Biology
#1,863
of 2,961 outputs
Outputs of similar age
#48,533
of 154,683 outputs
Outputs of similar age from Conservation Biology
#25
of 42 outputs
Altmetric has tracked 15,156,856 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,961 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.4. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 154,683 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.