↓ Skip to main content

Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics

Overview of attention for article published in Ecology and Evolution, October 2017
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

policy
1 policy source
twitter
1 X user
facebook
1 Facebook page

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
68 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.
Title
Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics
Published in
Ecology and Evolution, October 2017
DOI 10.1002/ece3.3469
Pubmed ID
Authors

Florent Bled, Jerrold L. Belant, Lawrence J. Van Daele, Nathan Svoboda, David Gustine, Grant Hilderbrand, Victor G. Barnes

Abstract

Current management of large carnivores is informed using a variety of parameters, methods, and metrics; however, these data are typically considered independently. Sharing information among data types based on the underlying ecological, and recognizing observation biases, can improve estimation of individual and global parameters. We present a general integrated population model (IPM), specifically designed for brown bears (Ursus arctos), using three common data types for bear (U. spp.) populations: repeated counts, capture-mark-recapture, and litter size. We considered factors affecting ecological and observation processes for these data. We assessed the practicality of this approach on a simulated population and compared estimates from our model to values used for simulation and results from count data only. We then present a practical application of this general approach adapted to the constraints of a case study using historical data available for brown bears on Kodiak Island, Alaska, USA. The IPM provided more accurate and precise estimates than models accounting for repeated count data only, with credible intervals including the true population 94% and 5% of the time, respectively. For the Kodiak population, we estimated annual average litter size (within one year after birth) to vary between 0.45 [95% credible interval: 0.43; 0.55] and 1.59 [1.55; 1.82]. We detected a positive relationship between salmon availability and adult survival, with survival probabilities greater for females than males. Survival probabilities increased from cubs to yearlings to dependent young ≥2 years old and decreased with litter size. Linking multiple information sources based on ecological and observation mechanisms can provide more accurate and precise estimates, to better inform management. IPMs can also reduce data collection efforts by sharing information among agencies and management units. Our approach responds to an increasing need in bear populations' management and can be readily adapted to other large carnivores.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 25%
Researcher 9 13%
Student > Master 8 12%
Student > Doctoral Student 4 6%
Other 4 6%
Other 11 16%
Unknown 15 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 51%
Environmental Science 11 16%
Psychology 1 1%
Earth and Planetary Sciences 1 1%
Engineering 1 1%
Other 0 0%
Unknown 19 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 28 November 2018.
All research outputs
#7,780,614
of 25,382,440 outputs
Outputs from Ecology and Evolution
#4,092
of 8,478 outputs
Outputs of similar age
#115,985
of 333,588 outputs
Outputs of similar age from Ecology and Evolution
#112
of 249 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 8,478 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.0. This one has gotten more attention than average, scoring higher than 51% of its peers.
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 333,588 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 64% of its contemporaries.
We're also able to compare this research output to 249 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.