Whitaker, J. O., McCracken, G. F. & Siemers, B. M. Food habits analysis of insectivorous bats. in Ecological and Behavioral Methods for the Study of Bats. 567–592. (2011).
Clare, E. L., Barber, B. R., Sweeney, B. W., Hebert, P. D. N. & Fenton, M. B. Eating local: Influences of habitat on the diet of little brown bats (Myotis lucifugus). Mol. Ecol. 20(8), 1772–1780. https://doi.org/10.1111/j.1365-294X.2011.05040.x (2011).
Google Scholar
Clare, E. L. et al. The diet of Myotis lucifugus across Canada: Assessing foraging quality and diet variability. Mol. Ecol. 23(15), 3618–3632. https://doi.org/10.1111/mec.12542 (2014).
Google Scholar
Wray, A. K. et al. Predator preferences shape the diets of arthropodivorous bats more than quantitative local prey abundance. Mol. Ecol. https://doi.org/10.1111/mec.15769 (2020).
Google Scholar
Agosta, S. J., Morton, D. & Kuhn, K. M. Feeding ecology of the bat Eptesicus fuscus: ‘Preferred’ prey abundance as one factor influencing prey selection and diet breadth. J. Zool. 260(2), 169–177. https://doi.org/10.1017/S0952836903003601 (2003).
Google Scholar
Clare, E. L., Symondson, W. O. C. & Fenton, M. B. An inordinate fondness for beetles? Variation in seasonal dietary preferences of night-roosting big brown bats (Eptesicus fuscus). Mol. Ecol. 23(15), 3633–3647. https://doi.org/10.1111/mec.12519 (2014).
Google Scholar
O’Rourke, D. R. et al. Lord of the Diptera (and moths and a spider): Molecular diet analyses and foraging ecology of Indiana bats in Illinois. Front. Ecol. Evol. 9, 12 (2021).
Google Scholar
Hope, P. R. et al. Second generation sequencing and morphological faecal analysis reveal unexpected foraging behaviour by Myotis nattereri (Chiroptera, Vespertilionidae) in winter. Front. Zool. 11(1), 39. https://doi.org/10.1186/1742-9994-11-39 (2014).
Google Scholar
Vesterinen, E. J., Puisto, A. I. E., Blomberg, A. S. & Lilley, T. M. Table for five, please: Dietary partitioning in boreal bats. Ecol. Evol. 8, 10914–10937 (2018).
Google Scholar
Vesterinen, E. J. et al. What you need is what you eat? Prey selection by the bat Myotis daubentonii. Mol. Ecol. 25, 1581–1594 (2016).
Google Scholar
Barclay, R. M. R. Population structure of temperate zone insectivorous bats in relation to foraging behaviour and energy demand. J. Anim. Ecol. 60(1), 165. https://doi.org/10.2307/5452 (1991).
Google Scholar
Fraser, E. E. & Fenton, M. B. Age and food hardness affect food handling by insectivorous bats. Can. J. Zool. 85, 985–993 (2007).
Google Scholar
von Frenckell, B. & Barclay, R. M. R. Bat activity over calm and turbulent water. Can. J. Zool. 65, 219–222 (1987).
Google Scholar
Kaupas, L. A. & Barclay, R. M. R. Temperature-dependent consumption of spiders by little brown bats (Myotis lucifugus), but not northern long-eared bats (M. septentrionalis), in northern Canada. Can. J. Zool. 96(3), 261 (2018).
Google Scholar
Alberdi, A., Aizpurua, O., Gilbert, M. T. P. & Bohmann, K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol. Evol. 9, 134–147 (2018).
Google Scholar
Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2018).
Google Scholar
Kunz, T. H. & Whitaker, J. O. An evaluation of fecal analysis for determining food habits of insectivorous bats. Can. J. Zool. 61, 1317–1321 (1983).
Google Scholar
Hamilton, I. M. & Barclay, R. M. R. Diets of juvenile, yearling, and adult big brown bats (Eptesicus fuscus) in Southeastern Alberta. J. Mammal. 79(3), 764. https://doi.org/10.2307/1383087 (1998).
Google Scholar
Moosman, P. R., Thomas, H. H. & Veilleux, J. P. Food habits of eastern small-footed bats (Myotis leibii) in New Hampshire. Am. Midl. Nat. 158(2), 354–360 (2007).
Google Scholar
Ober, H. K. & Hayes, J. P. Prey selection by bats in forests of Western Oregon. J. Mammal. 89(5), 1191–1200. https://doi.org/10.1644/08-MAMM-A-025.1 (2008).
Google Scholar
Long, B. L., Kurta, A. & Clemans, D. L. Analysis of DNA from feces to identify prey of big brown bats (Eptesicus fuscus) caught in apple orchards. Am. Midl. Nat. 170(2), 287–297 (2013).
Google Scholar
Gordon, R. et al. Molecular diet analysis finds an insectivorous desert bat community dominated by resource sharing despite diverse echolocation and foraging strategies. Ecol. Evol. 9, 3117–3129 (2019).
Google Scholar
Alberdi, A. et al. Promises and pitfalls of using high-throughput sequencing for diet analysis. Mol. Ecol. Resour. 19, 327–348 (2019).
Google Scholar
Clare, E. L. Molecular detection of trophic interactions: Emerging trends, distinct advantages, significant considerations and conservation applications. Evol. Appl. 7, 1144–1157 (2014).
Google Scholar
Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323(5911), 227–227. https://doi.org/10.1126/science.1163874 (2009).
Google Scholar
Frick, W. F. et al. Disease alters macroecological patterns of North American bats: Disease alters macroecology of bats. Glob. Ecol. Biogeogr. 24(7), 741–749. https://doi.org/10.1111/geb.12290 (2015).
Google Scholar
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).
Google Scholar
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).
Google Scholar
Anthony, E. L. P. & Kunz, T. H. Feeding strategies of the little brown bat, Myotis lucifugus, Southern New Hampshire. Ecology 58(4), 775–786. https://doi.org/10.2307/1936213 (1977).
Google Scholar
Pompanon, F. et al. Who is eating what: diet assessment using next generation sequencing. Mol. Ecol. 21, 1931–1950 (2012).
Google Scholar
Jusino, M. A. et al. An improved method for utilizing high-throughput amplicon sequencing to determine the diets of insectivorous animals. Mol. Ecol. Resour. 19, 176–190 (2019).
Google Scholar
O’Rourke, D. R., Bokulich, N. A., Jusino, M. A., MacManes, M. D., & Foster, J. T. A total crapshoot? Evaluating bioinformatic decisions in animal diet metabarcoding analyses. Ecol. Evolut. https://doi.org/10.1002/ece3.6594 (2020).
Langwig, K. E. et al. Resistance in persisting bat populations after white-nose syndrome invasion. Philos. Trans. R. Soc. B Biol. Sci. 372, 2160044 (2017).
Google Scholar
Maslo, B., Valent, M., Gumbs, J. F. & Frick, W. F. Conservation implications of ameliorating survival of little brown bats with white-nose syndrome. Ecol. Appl. 25, 1832–1840 (2015).
Google Scholar
Frick, W. F. et al. An emerging disease causes regional population collapse of a common North American bat species. Science 329(5992), 679–682. https://doi.org/10.1126/science.1188594 (2010).
Google Scholar
Turner, G. G., Reeder, D. M. & Coleman, J. T. H. A five-year assessment of mortality and geographic spread of white-nose syndrome in North American bats and a look to the future. Bat Res. News 52, 13–27 (2011).
Coleman, J. et al. A National Plan for Assisting States, Federal Agencies, and Tribes in Managing White-Nose Syndrome in Bats. https://s3.us-west-2.amazonaws.com/prod-is-cms-assets/wns/prod/b0634260-77d3-11e8-b37b-4f3513704a5e-white-nose_syndrome_national_plan_may_2011.pdf (2011).
Szymanski, J. A., Runge, M. C., Parkin, M. J. & Armstrong, M. White-Nose Syndrome Management: Report on Structured Decision Making Initiative. Vol. 51. http://pubs.er.usgs.gov/publication/70003465 (2009).
Kunz, T. H., Braun de Torrez, E., Bauer, D., Lobova, T. & Fleming, T. H. Ecosystem services provided by bats. Ann. N. Y. Acad. Sci. 1223, 1–38 (2011).
Google Scholar
Boyles, J. G., Cryan, P. M., McCracken, G. F. & Kunz, T. H. Economic importance of bats in agriculture. Science 332(6025), 41–42. https://doi.org/10.1126/science.1201366 (2011).
Google Scholar
Agosta, S. J. & Morton, D. Diet of the big brown bat, Eptesicus fuscus, from Pennsylvania and Western Maryland. Northeast. Nat. 10(1), 89–104 (2003).
Google Scholar
Brown, V. A., Braun de Torrez, E. & McCracken, G. F. Crop pests eaten by bats in organic pecan orchards. Crop Prot. 67, 66–71 (2015).
Google Scholar
Williams-Guillén, K., Perfecto, I. & Vandermeer, J. Bats limit insects in a Neotropical agroforestry system. Science 320(5872), 70–70. https://doi.org/10.1126/science.1152944 (2008).
Google Scholar
Held, D. W. & Ray, C. H. Asiatic garden beetle Maladera castanea (Coleoptera: Scarabaeidae) grubs found in damaged turf in Alabama. Fla. Entomol. 92(4), 670–672 (2009).
Google Scholar
Forschler, B. T. & Gardner, W. A. A review of the scientific literature on the biology and distribution of the genus Phyllophaga (Coleoptera: Scarabaeidae) in the Southeastern United States. J. Entomol. Sci. 25(4), 628–651. https://doi.org/10.18474/0749-8004-25.4.628 (1990).
Google Scholar
United States Forest Service. White Grubs in Forest Tree Nurseries and Plantations. Vol. 4. https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsbdev2_043588.pdf (1961).
Chandler, D. University of New Hampshire—Entomology Collection. UNH Insect and Arachnid Collections. https://duncan.unh.edu/ento/home.php (2020).
United States Forest Service. The Early Warning System for Forest Health Threads in the United States. https://www.fs.fed.us/foresthealth/publications/EWS_final_draft.pdf (2004).
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K., & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79(17), 5112–5120. https://doi.org/10.1128/AEM.01043-13 (2013).
Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
Google Scholar
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Google Scholar
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Google Scholar
Ratnasingham, S. & Hebert, P. D. N. bold: The barcode of life data system. http://www.barcodinglife.org. Mol. Ecol. Notes 7, 355–364 (2007).
Robeson, M. S. et al. RESCRIPt: Reproducible sequence taxonomy reference database management for the masses. bioRxiv. https://doi.org/10.1101/2020.10.05.326504 (2020).
Google Scholar
Chamberlain, S. BOLD: Interface to BOLD Systems API. (2017).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30(4), 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
Google Scholar
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).
Google Scholar
Beule, L. & Karlovsky, P. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ 8, e9593 (2020).
Google Scholar
Oksanen, J. et al. vegan: Community Ecology Package. (2018).
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).
Google Scholar
Cáceres, M. D. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology 90, 3566–3574 (2009).
Google Scholar
McKinney, W. Data structures for statistical computing in Python. Proc. Python Sci. Conf. https://doi.org/10.25080/Majora-92bf1922-00a (2010).
Google Scholar
McDonald, D. et al. The Biological Observation Matrix (BIOM) format or: How I learned to stop worrying and love the ome-ome. GigaScience 1, 7 (2012).
Google Scholar
Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
Google Scholar
Battaglia, T. btools: A Suite of R Function for All Types of Microbial Diversity Analyses. (2020).
Wilke, C. O. cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. (2017).
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
Google Scholar
Ogle, D. H. & Wheeler, P. FSA: Fisheries Stock Analysis. (2018).
Bisanz, J. E. qiime2R: Importing QIIME2 Artifacts and Associated Data into R Sessions. (2018).
Kahle, D. & Wickham, H. ggmap: Spatial visualization with ggplot2. R J. 5, 144–161 (2013).
Google Scholar
Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. (2018).
Slowikowski, K. ggrepel: Automatically Position Non-Overlapping Text Labels with ‘ggplot2’. (2018).
Hesselbarth, M. H. K., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: an open-source R tool to calculate landscape metrics. Ecography 42, 1648–1657 (2019).
Google Scholar
Grolemund, G., & Wickham, H. Dates and times made easy with lubridate. J. Stat. Softw. 40(3). https://www.jstatsoft.org/index.php/jss/article/view/v040i03/v40i03.pdf (2011).
Makiyama, K. magicfor: Magic Functions to Obtain Results from for Loops. (2016).
Bates, D. & Maechler, M. Matrix: Sparse and Dense Matrix Classes and Methods. (2018).
Graves, S., Piepho, H.-P. & Selzer, L. multcompView: Visualizations of Paired Comparisons. (2019).
Martinez Arbizu, P. pairwiseAdonis: Pairwise Multilevel Comparison using Adonis. (2017).
Hijmans, R. J. raster: Geographic Data Analysis and Modeling. (2020).
Wickham, H. Reshaping data with the reshape Package. J. Stat. Softw. 21(1), 1–20. https://doi.org/10.18637/jss.v021.i12 (2007).
Google Scholar
Wickham, H. scales: Scale Functions for Visualization. (2018).
Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439 (2018).
Google Scholar
Wickham, H. et al. svglite: An ‘SVG’ Graphics Device. (2020).
Wickham, H. tidyverse: Easily Install and Load the ‘Tidyverse’. (2017).
Strochak, S., Ueyama, K. & Williams, A. urbnmapr: State and County Shapefiles in sf and Tibble Format. (2020).
Bittinger, K. usedist: Distance Matrix Utilities. (2020).
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