{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T23:47:54Z","timestamp":1771890474832,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T00:00:00Z","timestamp":1569283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vegetation structure is a crucial component of habitat selection for many taxa, and airborne LiDAR (Light Detection and Ranging) technology is increasingly used to measure forest structure. Many studies have examined the relationship between LiDAR-derived structural characteristics and wildlife, but few have examined those characteristics in relation to small mammals, specifically, small mammal diversity. The aim of this study was to determine if LiDAR could predict small mammal diversity in a temperate-mixed forest community in Northern Wisconsin, USA, and which LiDAR-derived structural variables best predict small mammal diversity. We calculated grid metrics from LiDAR point cloud data for 17 plots in three differently managed sites and related the metrics to small mammal diversity calculated from five months of small mammal trapping data. We created linear models, then used model selection and multi-model inference as well as model fit metrics to determine if LiDAR-derived structural variables could predict small mammal diversity. We found that small mammal diversity could be predicted by LiDAR-derived variables including structural diversity, cover, and canopy complexity as well as site (as a proxy for management). Structural diversity and canopy complexity were positively related with small mammal diversity, while cover was negatively related to small mammal diversity. Although this study was conducted in a single habitat type during a single season, it demonstrates that LiDAR can be used to predict small mammal diversity in this location and possibly can be expanded to predict small mammal diversity across larger spatial scales.<\/jats:p>","DOI":"10.3390\/rs11192222","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T03:51:18Z","timestamp":1569383478000},"page":"2222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Lidar Prediction of Small Mammal Diversity in Wisconsin, USA"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6505-8233","authenticated-orcid":false,"given":"Sarah L.","family":"Schooler","sequence":"first","affiliation":[{"name":"Wildlife Department, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harold S. J.","family":"Zald","sequence":"additional","affiliation":[{"name":"Forestry Department, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"594","DOI":"10.2307\/1932254","article-title":"On bird species diversity","volume":"42","author":"MacAthur","year":"1961","journal-title":"Ecology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1046\/j.0305-0270.2003.00994.x","article-title":"Animal species diversity driven by habitat heterogeneity\/diversity: The importance of keystone structures","volume":"31","author":"Tews","year":"2004","journal-title":"J. Biogeogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.ecolind.2016.02.057","article-title":"A forest structure habitat index based on airborne laser scanning data","volume":"67","author":"Coops","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vogeler, J.C., and Cohen, W.B. (2016). A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models. Span. Assoc. Remote Sens., 1\u201314.","DOI":"10.4995\/raet.2016.3981"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4233","DOI":"10.3390\/rs70404233","article-title":"Can airborne laser scanning (ALS) and forest estimates derived from satellite images be used to predict abundance and species richness of birds and beetles in boreal forest?","volume":"7","author":"Lindberg","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1890\/1051-0761(2000)010[1367:SMASSI]2.0.CO;2","article-title":"Small mammals and stand structure in young pine, seed-tree, and old-growth forest, southwest Canada","volume":"10","author":"Sullivan","year":"2000","journal-title":"Ecol. Appl."},{"key":"ref_7","unstructured":"Thibault, K.M., Tsau, K., Springer, Y., and Knapp, L. (2017). TOS Protocol and Procedure: Small Mammal Sampling; revision J, National Ecological Observatory Network. NEON.DOC.000481."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1046\/j.1365-2664.2002.00759.x","article-title":"Population dynamics of small mammals in relation to forest age and structural habitat factors in northern Sweden","volume":"39","author":"Ecke","year":"2002","journal-title":"J. Appl. Ecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.foreco.2004.01.014","article-title":"Stand scale effects of partial harvesting and clearcutting on small mammals and forest structure","volume":"191","author":"Fuller","year":"2004","journal-title":"For. Ecol. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1644\/13-MAMM-A-025","article-title":"Habitat associations and assemblages of small mammals in natural plant communities of Wisconsin","volume":"95","author":"Stephens","year":"2014","journal-title":"J. Mammal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jaime-Gonz\u00e1lez, C., Acebes, P., Mateos, A., and Mezquida, E.T. (2017). Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0182451"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s13364-015-0215-3","article-title":"Remote sensing variables as predictors of habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling mammal living in a desert environment","volume":"60","author":"Campos","year":"2015","journal-title":"Mammal Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.ecolmodel.2014.07.004","article-title":"Mapping and monitoring Mount Graham red squirrel habitat with Lidar and Landsat imagery","volume":"289","author":"Hatten","year":"2014","journal-title":"Ecol. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/S0169-5347(02)02541-7","article-title":"Closing the seed dispersal loop","volume":"17","author":"Wang","year":"2002","journal-title":"Trends Ecol. Evol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e01777","DOI":"10.1002\/ecs2.1777","article-title":"Small mammal activity alters plant community composition and microbial activity in an old-field ecosystem","volume":"8","author":"Moorhead","year":"2017","journal-title":"Ecosphere"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1126\/science.250.4988.1705","article-title":"Control of a desert-grassland transition by a keystone rodent guild","volume":"250","author":"Brown","year":"1990","journal-title":"Science"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1111\/j.1365-2907.2011.00189.x","article-title":"Habitat heterogeneity and mammalian predator-prey interactions","volume":"42","author":"Gorini","year":"2012","journal-title":"Mammal Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1890\/04-0922","article-title":"Effects of biodiversity on ecosystem functioning: A consensus of current knowledge","volume":"75","author":"Hooper","year":"2005","journal-title":"Ecol. Monogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1002\/eap.1799","article-title":"Predation-mediated ecosystem services and disservices in agricultural landscapes","volume":"28","author":"Tschumi","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1111\/2041-210X.12219","article-title":"Applications of airborne lidar for the assessment of animal species diversity","volume":"5","author":"Simonson","year":"2014","journal-title":"Methods Ecol. Evol."},{"key":"ref_21","unstructured":"Burns, K.J. (2009). Treehaven Experimental Forest Land Management Plan, UW-Stevens Point College of Natural Resources."},{"key":"ref_22","unstructured":"(2019, April 20). National Ecological Observatory Network Field Sites Information. Available online: https:\/\/www.neonscience.org\/field-sites\/."},{"key":"ref_23","unstructured":"National Forest Service (2004). Management Area Direction."},{"key":"ref_24","unstructured":"National Ecological Observatory Network (2018, March 04). Data Product: TOS Sampling Site Locations. Available online: https:\/\/www.neonscience.org\/data\/spatial-data-maps\/."},{"key":"ref_25","unstructured":"National Ecological Observatory Network (2018, March 04). Data Products: DP3.30003, DP1.10072. Available online: http:\/\/data.neonscience.org."},{"key":"ref_26","unstructured":"Krause, K., and Goulden, T. (2015). NEON L0-to-L1 Discrete Return LiDAR Algorithm Theoretical Basis Document; revision A, National Ecological Observatory Network. NEON.DOC.001292."},{"key":"ref_27","unstructured":"Goulden, T., and Hass, B. (2016). NEON AOP LMS QA\/QC Report for Domain 05, National Ecological Observatory Network."},{"key":"ref_28","unstructured":"Goulden, T., and Hass, B. (2016). NEON AOP QA Report for Domain 5, National Ecological Observatory Network."},{"key":"ref_29","unstructured":"Goulden, T. (2019). NEON Elevation (DTM and DSM) Algorithm Theoretical Basis Document, National Ecological Observatory Network."},{"key":"ref_30","unstructured":"Azuaje, E., Jones, K., Barnett, D., Meier, C., Krouss, R., and McKay, J. (2015). TOS Protocol and Procedure: Plot Establishment Revision D, National Ecological Observatory Network."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1038\/nature00840","article-title":"Fractal geometry predicts verying body size scaling relationships for mammal and bird home ranges","volume":"418","author":"Haskell","year":"2002","journal-title":"Nature"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1002\/jwmg.613","article-title":"Short-term responses of small mammals to timber harvest in the United States Central Hardwood Forest Region","volume":"77","author":"Kellner","year":"2013","journal-title":"J. Wildl. Manag."},{"key":"ref_33","unstructured":"Roussel, J.-R., and Auty, D. (2019, April 20). lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. R Package Version 1.4.1. Available online: https:\/\/CRAN.R-project.org\/package=lidR."},{"key":"ref_34","unstructured":"R Core Team (2018). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_35","unstructured":"Hijmans, R.J. (2019, April 20). raster: Geographic Data Analysis and Modeling. R Package Version 3.0.2. Available online: https:\/\/CRAN.R-project.org\/package=raster."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.3390\/rs70403446","article-title":"Airborne lidar for woodland habitat quality monitoring: Exploring the significance of lidar data characteristics when modelling organism-habitat relationships","volume":"7","author":"Hill","year":"2015","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"153","DOI":"10.5589\/m06-005","article-title":"Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data","volume":"32","author":"Falkowski","year":"2005","journal-title":"Can. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pretzsch, H. (2009). Description and analysis of stand structures. Forest Dynamics, Growth and Yield, Springer.","DOI":"10.1007\/978-3-540-88307-4"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rse.2018.06.023","article-title":"Quantifying understory vegetation density using small-footprint airborne lidar","volume":"215","author":"Campbell","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","first-page":"261","article-title":"Characterizing forest succession in central Ontario using Lidar-derived Indices","volume":"77","author":"Treitz","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1111\/1755-0998.12004","article-title":"Testing a user-defined selection of diversity indices","volume":"12","author":"Pallmann","year":"2012","journal-title":"Mol. Ecol. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1111\/j.2006.0030-1299.14714.x","article-title":"Entropy and diversity","volume":"113","author":"Jost","year":"2006","journal-title":"Oikos"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1890\/06-1736.1","article-title":"Partitioning diversity into independent alpha and beta components","volume":"88","author":"Jost","year":"2007","journal-title":"Ecology"},{"key":"ref_44","first-page":"498","article-title":"glmulti: Model selection and multimodel inference made easy 2013","volume":"1","author":"Calcagno","year":"2018","journal-title":"R Package Version"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Anderson, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data.","DOI":"10.3133\/pp964"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2018.09.006","article-title":"A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies","volume":"146","author":"Yang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","unstructured":"Burnham, K.P., and Anderson, D.D. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, Springer. [2nd ed.]."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"603","DOI":"10.2307\/2533961","article-title":"Model selection: An integral part of inference","volume":"53","author":"Buckland","year":"1997","journal-title":"Biometrics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.1016\/j.rse.2007.08.023","article-title":"The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine\/aspen forest","volume":"112","author":"Clawges","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.rse.2006.11.016","article-title":"Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA","volume":"108","author":"Goetz","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.rse.2015.12.038","article-title":"From field surveys to LiDAR: Shining a light on how bats respond to forest structure","volume":"175","author":"Froidevaux","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.rse.2005.02.012","article-title":"Locating and estimating the extent of Delmarva fox squirrel habitat using an airborne LiDAR profiler","volume":"96","author":"Nelson","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.2307\/3803050","article-title":"Induced spatial heterogeneity in forest canopies: Responses of small mammals","volume":"65","author":"Carey","year":"2010","journal-title":"J. Wildl. Manag."},{"key":"ref_54","first-page":"3","article-title":"Ecological scale and forest development: Squirrels, dietary fungi, and vascular plants in managed and unmanaged forests","volume":"142","author":"Carey","year":"1999","journal-title":"Wildl. Monogr."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10342-013-0761-7","article-title":"Responses of small mammals to clear-cutting in temperate and boreal forests of Europe: A meta-analysis and review","volume":"133","author":"Bogdziewicz","year":"2014","journal-title":"Eur. J. For. Res."},{"key":"ref_56","unstructured":"Nelson, D.L. (2017). Demographic Responses of Small Mammals to Distrurbance Induced by Forest Management. [Master\u2019s Thesis, Purdue University]."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2533","DOI":"10.1016\/j.rse.2009.07.002","article-title":"Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability","volume":"113","author":"Martinuzzi","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.3390\/rs70201877","article-title":"Terrestrial laser scanning as an effective tool to retrieve tree level height, crown width, and stem diameter","volume":"7","author":"Srinivasan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.tree.2018.12.012","article-title":"Advances in Microclimate Ecology Arising from Remote Sensing","volume":"34","author":"Zellweger","year":"2019","journal-title":"Trends Ecol. Evol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.isprsjprs.2017.10.002","article-title":"Mapping the height and spatial cover of features beneath the forest canopy at small-scales using airborne scanning discrete return Lidar","volume":"133","author":"Sumnall","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","unstructured":"Mitchell, B., Walterman, M., Mellin, T., Wilcox, C., Lynch, A.M., Anhold, J., Falk, D.A., Koprowski, J., Laes, D., and Evans, D. (2012). Mapping Vegetation Structure in the Pinale\u00f1o Mountains Using Lidar\u2014Phase 3: Forest Inventory Modeling."},{"key":"ref_62","first-page":"165","article-title":"Abundance and Diversity of Small Mammals in Relation to Structural Habitat Factors","volume":"49","author":"Ecke","year":"2001","journal-title":"Ecol. Bull."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1111\/j.1466-8238.2007.00287.x","article-title":"Landscape modification and habitat fragmentation: A synthesis","volume":"16","author":"Fischer","year":"2007","journal-title":"Glob. Ecol. Biogeogr."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2222\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:23:29Z","timestamp":1760189009000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,24]]},"references-count":63,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11192222"],"URL":"https:\/\/doi.org\/10.3390\/rs11192222","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,24]]}}}