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Novel and Lost Forests in the Upper Midwestern United States, from New Estimates of Settlement-Era Composition, Stem Density, and Biomass

Overview of attention for article published in PLOS ONE, December 2016
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

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54 tweeters

Citations

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39 Dimensions

Readers on

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74 Mendeley
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Title
Novel and Lost Forests in the Upper Midwestern United States, from New Estimates of Settlement-Era Composition, Stem Density, and Biomass
Published in
PLOS ONE, December 2016
DOI 10.1371/journal.pone.0151935
Pubmed ID
Authors

Simon J. Goring, David J. Mladenoff, Charles V. Cogbill, Sydne Record, Christopher J. Paciorek, Stephen T. Jackson, Michael C. Dietze, Andria Dawson, Jaclyn Hatala Matthes, Jason S. McLachlan, John W. Williams

Abstract

EuroAmerican land-use and its legacies have transformed forest structure and composition across the United States (US). More accurate reconstructions of historical states are critical to understanding the processes governing past, current, and future forest dynamics. Here we present new gridded (8x8km) reconstructions of pre-settlement (1800s) forest composition and structure from the upper Midwestern US (Minnesota, Wisconsin, and most of Michigan), using 19th Century Public Land Survey System (PLSS), with estimates of relative composition, above-ground biomass, stem density, and basal area for 28 tree types. This mapping is more robust than past efforts, using spatially varying correction factors to accommodate sampling design, azimuthal censoring, and biases in tree selection. We compare pre-settlement to modern forests using US Forest Service Forest Inventory and Analysis (FIA) data to show the prevalence of lost forests (pre-settlement forests with no current analog), and novel forests (modern forests with no past analogs). Differences between pre-settlement and modern forests are spatially structured owing to differences in land-use impacts and accompanying ecological responses. Modern forests are more homogeneous, and ecotonal gradients are more diffuse today than in the past. Novel forest assemblages represent 28% of all FIA cells, and 28% of pre-settlement forests no longer exist in a modern context. Lost forests include tamarack forests in northeastern Minnesota, hemlock and cedar dominated forests in north-central Wisconsin and along the Upper Peninsula of Michigan, and elm, oak, basswood and ironwood forests along the forest-prairie boundary in south central Minnesota and eastern Wisconsin. Novel FIA forest assemblages are distributed evenly across the region, but novelty shows a strong relationship to spatial distance from remnant forests in the upper Midwest, with novelty predicted at between 20 to 60km from remnants, depending on historical forest type. The spatial relationships between remnant and novel forests, shifts in ecotone structure and the loss of historic forest types point to significant challenges for land managers if landscape restoration is a priority. The spatial signals of novelty and ecological change also point to potential challenges in using modern spatial distributions of species and communities and their relationship to underlying geophysical and climatic attributes in understanding potential responses to changing climate. The signal of human settlement on modern forests is broad, spatially varying and acts to homogenize modern forests relative to their historic counterparts, with significant implications for future management.

Twitter Demographics

The data shown below were collected from the profiles of 54 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 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 73 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 22%
Student > Ph. D. Student 12 16%
Student > Master 12 16%
Student > Bachelor 8 11%
Student > Doctoral Student 4 5%
Other 11 15%
Unknown 11 15%
Readers by discipline Count As %
Environmental Science 17 23%
Earth and Planetary Sciences 15 20%
Agricultural and Biological Sciences 12 16%
Engineering 5 7%
Business, Management and Accounting 2 3%
Other 11 15%
Unknown 12 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 29 October 2021.
All research outputs
#882,472
of 19,512,142 outputs
Outputs from PLOS ONE
#12,772
of 173,158 outputs
Outputs of similar age
#25,459
of 409,842 outputs
Outputs of similar age from PLOS ONE
#316
of 5,379 outputs
Altmetric has tracked 19,512,142 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 173,158 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.0. This one has done particularly well, scoring higher than 92% 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 409,842 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 93% of its contemporaries.
We're also able to compare this research output to 5,379 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 94% of its contemporaries.