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Quantifying drivers of wild pig movement across multiple spatial and temporal scales

Overview of attention for article published in Movement Ecology, June 2017
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Title
Quantifying drivers of wild pig movement across multiple spatial and temporal scales
Published in
Movement Ecology, June 2017
DOI 10.1186/s40462-017-0105-1
Pubmed ID
Authors

Shannon L. Kay, Justin W. Fischer, Andrew J. Monaghan, James C. Beasley, Raoul Boughton, Tyler A. Campbell, Susan M. Cooper, Stephen S. Ditchkoff, Steve B. Hartley, John C. Kilgo, Samantha M. Wisely, A. Christy Wyckoff, Kurt C. VerCauteren, Kim M. Pepin

Abstract

The movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management. We obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season. We found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales. The analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Poland 1 <1%
Unknown 110 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 29 26%
Student > Ph. D. Student 23 21%
Researcher 18 16%
Student > Bachelor 10 9%
Other 6 5%
Other 12 11%
Unknown 13 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 51%
Environmental Science 16 14%
Social Sciences 3 3%
Earth and Planetary Sciences 3 3%
Veterinary Science and Veterinary Medicine 3 3%
Other 5 5%
Unknown 24 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 04 August 2017.
All research outputs
#6,623,989
of 11,564,189 outputs
Outputs from Movement Ecology
#81
of 120 outputs
Outputs of similar age
#128,277
of 266,277 outputs
Outputs of similar age from Movement Ecology
#4
of 5 outputs
Altmetric has tracked 11,564,189 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 120 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one is in the 27th percentile – i.e., 27% 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 266,277 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.