in

Spatial and temporal variation in New Hampshire bat diets

  • 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).

    CAS 
    Article 
    PubMed 

    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).

    Article 
    PubMed 

    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).

    Article 
    PubMed 
    PubMed Central 

    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).

    Article 

    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).

    Article 
    PubMed 

    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).

    ADS 

    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).

    Article 
    PubMed 
    PubMed Central 

    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).

    Article 

    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).

    CAS 
    Article 

    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).

    Article 

    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).

    Article 

    Google Scholar 

  • von Frenckell, B. & Barclay, R. M. R. Bat activity over calm and turbulent water. Can. J. Zool. 65, 219–222 (1987).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    Google Scholar 

  • Alberdi, A. et al. Promises and pitfalls of using high-throughput sequencing for diet analysis. Mol. Ecol. Resour. 19, 327–348 (2019).

    Article 

    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).

    Article 

    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).

    CAS 
    Article 
    PubMed 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    Google Scholar 

  • Pompanon, F. et al. Who is eating what: diet assessment using next generation sequencing. Mol. Ecol. 21, 1931–1950 (2012).

    CAS 
    Article 

    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).

    CAS 
    Article 

    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).

    Article 

    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).

    Article 

    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).

    ADS 
    CAS 
    Article 
    PubMed 

    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).

    Google Scholar 

  • 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).

    ADS 
    Article 

    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).

    ADS 
    Article 
    PubMed 

    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).

    Article 

    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).

    Article 

    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).

    ADS 
    CAS 
    Article 
    PubMed 

    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).

    Article 

    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).

    Article 

    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).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article 

    Google Scholar 

  • Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    CAS 
    Article 

    Google Scholar 

  • Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS 
    Article 

    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).

    Article 

    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).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).

    Article 

    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).

    Article 

    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).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Cáceres, M. D. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology 90, 3566–3574 (2009).

    Article 

    Google Scholar 

  • McKinney, W. Data structures for statistical computing in Python. Proc. Python Sci. Conf. https://doi.org/10.25080/Majora-92bf1922-00a (2010).

    Article 

    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).

    Article 

    Google Scholar 

  • Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

    CAS 
    Article 

    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).

    Article 

    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).

    Article 

    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).

    Article 

    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).

    MathSciNet 
    Article 

    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).

    Article 

    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).


  • Source: Ecology - nature.com

    New data from the first discovered paleoparadoxiid (Desmostylia) specimen shed light into the morphological variation of the genus Neoparadoxia

    Using seismology for groundwater management