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ENDOGENOUS AND EXOGENOUS FACTORS CONTROLLING TEMPORAL ABUNDANCE PATTERNS OF TROPICAL MOSQUITOES

Overview of attention for article published in Ecological Applications, December 2008
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
3 tweeters

Citations

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36 Dimensions

Readers on

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69 Mendeley
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Title
ENDOGENOUS AND EXOGENOUS FACTORS CONTROLLING TEMPORAL ABUNDANCE PATTERNS OF TROPICAL MOSQUITOES
Published in
Ecological Applications, December 2008
DOI 10.1890/07-1209.1
Pubmed ID
Authors

Guo-Jing Yang, Barry W. Brook, Peter I. Whelan, Sam Cleland, Corey J. A. Bradshaw, Yang, Guo-Jing, Brook, Barry W., Whelan, Peter I., Cleland, Sam, Bradshaw, Corey J. A.

Abstract

The growing demand for efficient and effective mosquito control requires a better understanding of vector population dynamics and how these are modified by endogenous and exogenous factors. A long-term (11-year) monitoring data set describing the relative abundance of the saltmarsh mosquito (Aedes vigilax) in the greater Darwin region, northern Australia, was examined in a suite of Gompertz-logistic (GL) models with and without hypothesized environmental correlates (high tide frequency, rainfall, and relative humidity). High tide frequency and humidity were hypothesized to influence saltmarsh mosquito abundance positively, and rainfall was hypothesized to correlate negatively by reducing the availability of suitable habitats (moist substrata) required by ovipositing adult female mosquitoes. We also examined whether environmental correlates explained the variance in seasonal carrying capacity (K) because environmental stochasticity is hypothesized to modify population growth rate (r), carrying capacity, or both. Current and lagged-time effects were tested by comparing alternative population dynamics models using three different information criteria (Akaike's Information Criterion [corrected; AIC(c)], Bayesian Information Criterion [BIC], and cross-validation [C-V]). The GL model with a two-month lag without environmental effects explained 31% of the deviance in population growth rate. This increased to > 70% under various model combinations of high tide frequency, rainfall, and relative humidity, of which, high tide frequency and rainfall had the highest contributions. Temporal variation in K was explained weakly by high tide frequency, and there was some evidence that the filling of depressions to reduce standing water availability has reduced Aedes vigilax carrying capacity over the study period. This study underscores the need to consider simultaneously both types of drivers (endogenous and exogenous) when predicting mosquito abundance and population growth patterns. This work also indicates that climate change, via continued increases in rainfall and higher expected frequencies and intensities of high tide events with sea level rise, will alter mosquito abundance trends in northern Australia.

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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 2 3%
Italy 1 1%
Mexico 1 1%
United Kingdom 1 1%
Brazil 1 1%
Japan 1 1%
United States 1 1%
Unknown 61 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 32%
Student > Ph. D. Student 13 19%
Student > Master 9 13%
Professor 6 9%
Student > Doctoral Student 5 7%
Other 14 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 48%
Environmental Science 13 19%
Medicine and Dentistry 5 7%
Unspecified 4 6%
Social Sciences 4 6%
Other 10 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 March 2013.
All research outputs
#667,926
of 8,098,128 outputs
Outputs from Ecological Applications
#203
of 1,742 outputs
Outputs of similar age
#11,508
of 122,263 outputs
Outputs of similar age from Ecological Applications
#2
of 31 outputs
Altmetric has tracked 8,098,128 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,742 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has done well, scoring higher than 88% 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 122,263 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.