↓ Skip to main content

Inferring invasive species abundance using removal data from management actions

Overview of attention for article published in Ecological Applications, September 2016
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)

Mentioned by

3 tweeters


25 Dimensions

Readers on

58 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Inferring invasive species abundance using removal data from management actions
Published in
Ecological Applications, September 2016
DOI 10.1002/eap.1383
Pubmed ID

Amy J. Davis, Mevin B. Hooten, Ryan S. Miller, Matthew L. Farnsworth, Jesse Lewis, Michael Moxcey, Kim M. Pepin


Evaluation of the progress of management programs for invasive species is crucial for demonstrating impacts to stakeholders and strategic planning of resource allocation. Estimates of abundance before and after management activities can serve as a useful metric of population management programs. However, many methods of estimating population size are too labor intensive and costly to implement, posing restrictive levels of burden on operational programs. Removal models are a reliable method for estimating abundance before and after management using data from the removal activities exclusively, thus requiring no work in addition to management. We developed a Bayesian hierarchical model to estimate abundance from removal data accounting for varying levels of effort, and used simulations to assess the conditions under which reliable population estimates are obtained. We applied this model to estimate site-specific abundance of an invasive species, feral swine (Sus scrofa), using removal data from aerial gunning in 59 site/time-frame combinations (480-19,600 acres) throughout Oklahoma and Texas, USA. Simulations showed that abundance estimates were generally accurate when effective removal rates (removal rate accounting for total effort) were above 0.40. However, when abundances were small (<50) the effective removal rate needed to accurately estimates abundances was considerably higher (0.70). Based on our post-validation method, 78% of our site/time frame estimates were accurate. To use this modeling framework it is important to have multiple removals (more than three) within a time frame during which demographic changes are minimized (i.e., a closed population; ≤3 months for feral swine). Our results show that the probability of accurately estimating abundance from this model improves with increased sampling effort (8+ flight hours across the 3-month window is best) and increased removal rate. Based on the inverse relationship between inaccurate abundances and inaccurate removal rates, we suggest auxiliary information that could be collected and included in the model as covariates (e.g., habitat effects, differences between pilots) to improve accuracy of removal rates and hence abundance estimates.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 21%
Researcher 12 21%
Student > Ph. D. Student 10 17%
Student > Bachelor 6 10%
Other 3 5%
Other 5 9%
Unknown 10 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 52%
Environmental Science 7 12%
Biochemistry, Genetics and Molecular Biology 2 3%
Medicine and Dentistry 2 3%
Economics, Econometrics and Finance 1 2%
Other 2 3%
Unknown 14 24%

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 11 November 2016.
All research outputs
of 17,902,988 outputs
Outputs from Ecological Applications
of 2,897 outputs
Outputs of similar age
of 273,942 outputs
Outputs of similar age from Ecological Applications
of 40 outputs
Altmetric has tracked 17,902,988 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,897 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one is in the 26th percentile – i.e., 26% 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 273,942 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 57% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.