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Landscape Ecology

, Volume 30, Issue 4, pp 681–697 | Cite as

Predictive spatial niche and biodiversity hotspot models for small mammal communities in Alaska: applying machine-learning to conservation planning

  • Andrew P. BaltenspergerEmail author
  • Falk Huettmann
Research Article

Abstract

Context

Changing global environmental conditions, especially at northern latitudes, are threatening to shift species distributions and alter wildlife communities.

Objective

We aimed to establish current distributions and community arrangements of small mammals to provide important baselines for monitoring and conserving biodiversity into the future.

Methods

We used 4,408 archived museum and open-access records and the machine learning algorithm, RandomForests, to create high-resolution spatial niche models for 17 species of rodents and shrews in Alaska. Models were validated using independent trapping results from 20 locations stratified along statewide mega-transects, and an average species richness curve was calculated for field samples. Community cluster analyses (varclus) identified geographic patterns of sympatry among species. Species models were summed to create the first small-mammal species richness map for Alaska.

Results

Species richness increased logarithmically to a mean of 3.3 species per location over 1,500 trap-nights. Distribution models yielded mean accuracies of 71 % (45–90 %), and maps correctly predicted a mean of 75 % (60–95 %) of occurrences correctly in the field. Top predictors included Soil Type, Ecoregion, Landfire Land-cover, December Sea Ice, and July Temperature at the geographic scale. Cluster analysis delineated five community groups (3–4 species/group), and species richness was highest (11–13 species) over the Yukon-Tanana Uplands.

Conclusions

Models presented here provide spatial predictions of current small mammal biodiversity in Alaska and an initial framework for mapping and monitoring wildlife distributions across broad landscapes into the future.

Keywords

Arctic Boreal Forest Ecological niche modeling Lemmings Machine learning Mega-transect sampling Open-access data RandomForests Shrews Voles 

Notes

Acknowledgments

We are deeply indebted to everyone who assisted AB in the field, especially David Keiter, Edward Corp, Jessica Jordan, Alex Nicely, Casey Brown, Tim Mullet, László Kövér, and Dmitry Korobitsyn for their tireless efforts on the trap-lines. Thank you also to Link Olson, Stephen MacDonald, Joseph Cook, and GBIF for compiling and contributing valuable occurrence datasets and lending traps and equipment. Logistical support, field gear, and transportation were also generously provided by David Payer, Mark Bertram, Tom Paragi, Dashiel Feierabend, Joshua Jerome, Chris Long, Jeff Melegari, Jeremy Carlson, the Russian Mission Village Council and Kako Retreat Center. Finally, thank you to everyone who so generously offered help, advice, and fish along the Yukon River.

Supplementary material

10980_2014_150_MOESM1_ESM.xlsx (1.5 mb)
Supplementary material 1 (XLSX 1490 kb)
10980_2014_150_MOESM2_ESM.docx (19 kb)
Supplementary material 2 (DOCX 20 kb)
10980_2014_150_MOESM3_ESM.docx (676 kb)
Supplementary material 3 (DOCX 676 kb)
10980_2014_150_MOESM4_ESM.xlsx (51 kb)
Supplementary material 4 (XLSX 51 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Biology and WildlifeUniversity of Alaska FairbanksFairbanksUSA

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