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Model selection and assessment for multi‐species occupancy models

Overview of attention for article published in Ecology, July 2016
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6 X users

Citations

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108 Dimensions

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405 Mendeley
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Title
Model selection and assessment for multi‐species occupancy models
Published in
Ecology, July 2016
DOI 10.1890/15-1471.1
Pubmed ID
Authors

Kristin M Broms, Mevin B Hooten, Ryan M Fitzpatrick

Abstract

While multi-species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single-species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross-validation techniques performing the best in terms of prediction.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 405 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 1%
Switzerland 1 <1%
France 1 <1%
Portugal 1 <1%
Brazil 1 <1%
Latvia 1 <1%
Spain 1 <1%
United Kingdom 1 <1%
Unknown 393 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 94 23%
Researcher 78 19%
Student > Master 76 19%
Student > Doctoral Student 22 5%
Student > Bachelor 21 5%
Other 52 13%
Unknown 62 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 177 44%
Environmental Science 108 27%
Engineering 6 1%
Biochemistry, Genetics and Molecular Biology 5 1%
Earth and Planetary Sciences 4 <1%
Other 21 5%
Unknown 84 21%
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 08 March 2022.
All research outputs
#8,139,379
of 25,834,578 outputs
Outputs from Ecology
#3,290
of 6,925 outputs
Outputs of similar age
#123,246
of 368,767 outputs
Outputs of similar age from Ecology
#38
of 76 outputs
Altmetric has tracked 25,834,578 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 6,925 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.1. This one has gotten more attention than average, scoring higher than 50% 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 368,767 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 65% of its contemporaries.
We're also able to compare this research output to 76 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 50% of its contemporaries.