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Harvard Forest Data Archive

HF239

Ants in the New York Pine Barrens 2012-2013

Related Publications

Data

Overview

  • Lead: Grace Barber, Aaron Ellison
  • Investigators:
  • Contact: Aaron Ellison
  • Start date: 2012
  • End date: 2013
  • Status: completed
  • Location: Albany Pine Bush Preserve (NY), Saratoga Sand Plains (NY), Rome Sand Plains (NY)
  • Latitude: +42.67 to +43.24
  • Longitude: -75.58 to -73.68
  • Elevation: 90 to 130 meter
  • Taxa: Formicidae (ants)
  • Release date: 2015
  • Revisions:
  • EML file: knb-lter-hfr.239.4
  • DOI: digital object identifier
  • Related links:
  • Study type: short-term measurement
  • Research topic: biodiversity studies; conservation and management
  • LTER core area: populations, disturbance
  • Keywords: ants, community composition, human disturbance, pine, species composition
  • Abstract:

    Ants are ecologically important, environmentally sensitive, widespread, and abundant, yet ant assemblages of many habitats remain poorly understood. This is true of inland pine barrens in the northeastern United States. In the Northeast, such barrens support uncommon ant species and high species density for the region. Ants in inland barrens of New York State barely have been studied. To increase knowledge of these assemblages, I systematically collected ants from three NY inland barrens and investigated how hiking trails--a common man-made disturbance--may be impacting ant assemblages in these early-successional, disturbance-dependent ecosystems. My data strongly indicate uncommonly high densities of ant species in NY pine barrens, including the most northern known occurrences of two species, and show that hiking trails alter ant assemblage composition and species density.

  • Methods:

    Study plots

    I surveyed six plots in 2012, and six in 2013. The 2012 plots were divided among the three field sites — three in the APBP, two in the SSP, and one in the RSP — and all were in areas of relatively homogenous vegetation that ranged in size from 1.8 to 17.1 contiguous ha. The 2012 APBP plots were the Discovery Center Field (DC), Apollo Restoration (AR), and Baron’s Field (BF). The two at SSP were Camp Saratoga (CS) and Trinity (TR), and the sole plot at RSP was the Rome Sand Plains Field (RS). I selected flat, open areas, dominated by graminoids and heaths, with little or no cover of shrub-level oaks to maximize habitat similarity of plots across the three pine barrens systems.

    All plots sampled in 2013 were at ABPB, and included Blueberry Hill West (BH), Draperies (DP), Great Dune (GD), Karner Barrens East (KE), Karner Barrens West (KW), and King’s Road Barrens (KB); none of the 2012 plots were resampled in 2013. The 2013 plots were under active management aimed at creating and maintaining Pitch Pine-Scrub Oak habitat, which is characterized as being dominated by shrub-level oaks, herbs and heaths, and having a sparse over-story of Pitch Pine and oak species. All of the 2013 plots were bisected by a hiking trail, and, except for KE, all contained a substantial dune and correspondingly steep topography over portions of the plot.

    The soil underlying most of the study plots at the three sites is loamy fine sand that is well to excessively drained, rapidly permeable, with strong to medium acidity, and lacking gravel (Barnes 2003, SSSNRCS 2014). However, the three APBP plots surveyed in 2012 were located on areas that had been heavily impacted by human activity, and the soils at these sites are classified as Udipsamments (AR and DC) and Udorthents (BF) (SSSNRCS 2014). The Udipsamments soils of AR and DC differed from all other plots in that the top layer of soil was coarse sand rather than loam or loamy fine sand. The Udorthents soil of BF has an upper layer of loam, which is similar to most other plots, but is somewhat less well drained. Both of the SSP plots were on Oakville loamy fine sand, the RSP soil type was Windsor loamy fine sand, and the other APBP plots were primarily located on Colonie loamy fine sand (SSSNRCS 2014).

    Sampling design

    From May through August of 2012, I surveyed ants along transects summing to 140 m in length per plot. For plots that were less than 140-m long in any direction I used multiple, smaller transects, laid out in parallel across the plots and separated by a distance of 30 m so that the same total length of transect was sampled in every plot. All transects (or transect segments) were at least 10 m from the edges of the plot and the exact placement of the transect (or first transect segment — from which all others were based) was determined randomly. I placed twelve 1-m2 quadrats at 11-m intervals (10 m for the space between quadrats, plus 1 m for the quadrat) along each transect, and at the midpoint between each of the quadrats I placed a pitfall trap.

    In 2013, I placed three 120 m × 1 m transects within each plot. Each transect was placed so that it would fall under one of three conditions, “trail”, “edge”, or “interior”. These conditions were defined based on physical location relative to a hiking trail. The interior transects were oriented roughly parallel to the trail, but placed away from it by a distance of 35-45 m into the managed habitat. The distance of 35-45 m that I used in this study is enough to reasonably assume low probability of collecting foraging ants belonging to colonies located along hiking trails on the interior transects. The edge transects were located in the same managed habitat patch as the interior transects, but were placed 10 to 25 cm from the boundary between the habitat patch and the trail, such that the entire width of the transect was inside the managed habitat and at least 10 cm from the trail boundary. The boundary of the managed habitat was determined visually, based on vegetation height, which almost always increased suddenly and drastically at the trail edge (see image 1). The trail transects were placed along the margin of the hiking trail, so that one edge of the transect abutted the determined edge of the trail. I randomly determined the exact placement of the trail transect. The edge and interior transects were then placed relative to it.

    In 2013, ten 1-m2 quadrats were placed along each transect at 11-m intervals and the distance from one end of the transect to the first quadrat also was determined randomly. Each of the 2013 transects was surveyed twice: once in May–June, and again in July–August. The order of plot sampling was randomized during both surveys. The ten quadrats sampled along each transect during the second survey were offset from the ten sampled during the first survey by a distance of one meter (a full meter between the two proximate edges of the first- and second-survey quadrats) to reduce the effects of disturbance from the first survey.

    Ant collection methods

    Pitfall traps. In 2012 I sampled quadrats with pitfall traps consisting of 118-ml polypropylene cups (6-cm diameter) filled with ≈80 ml of a dilute solution of water and unscented, biodegradable detergent. I buried the cups in the ground so that the lip of the cup was level with the soil surface, and left the cups in the field with the lids on for three days of settling time to reduce the effect of disturbance on ant captures (the “digging-in effect”: Greenslade (1973)). After this period, I removed the lids and left the traps open to collect specimens for 48 hours during dry, warm weather. I then collected the traps and transferred the specimens to 95% ethyl-alcohol. I did not use pitfall traps in 2013 due to both time constraints and concerns about inadvertently trapping endangered Lycaeides melissa samuelis Nabokov (the Karner Blue Butterfly) larvae, among other rare and non-target species.

    Timed quadrat searches. In 2012 I searched each of the twelve 1-m2 quadrats per transect (one transect per plot) for 15 minutes; in 2013 I used 8-minute searches for ants in the twenty 1-m2 quadrats per transect (one transect per plot). The 2013 quadrat searches were done over two survey periods so that ten quadrats per transect were searched during each of the two surveys. My method was similar to that described as “quadrat sampling” in Agosti et al. (2000), except that I did not attempt to collect every ant observed, only representatives from each species and colony observed. I recorded which ants were clearly collected from colonies within the quadrats and which were not. Time spent recording and transferring specimens was not included in the search time. Visual searching and pitfall trapping both were done during dry weather, but were not done simultaneously.

    Litter sifting. Thorough quadrat searches provided a snapshot of all ants foraging and nesting within a given area, thereby generating a good estimate of species density. My quadrat searches were standardized by size across both years, and by time within years. However, most of the 2013 plots were in areas of high shrub-level oak density, and had correspondingly high quantities of leaf litter. The 2012 plots, conversely, had little leaf litter in most cases, or litter that consisted mainly of dead grasses and sedges. This difference in litter composition among quadrats in 2013 and between 2013 and 2012 quadrats affected the ease of searching for ants during the allotted time. The leaf litter from shrub-level oaks tended to provide more nesting and hiding opportunities for ants than did no litter or litter made up of dead grass, which meant that ants were more likely to be overlooked in the quadrats beneath shrub-level oaks. To maintain a similar level of search completeness across quadrats and habitat types, I added litter-sifting to the quadrat searches in 2013.

    During the 8-min quadrat searches in 2013, I collected all of the leaf litter from each quadrat and placed it into a wire-mesh, waste-paper basket set inside a white bucket. The mesh holes were parallelograms with corner-to-corner distances of 30 and 50 mm, which was large enough for the largest species (Camponotus americanus Mayr (The American Carpenter Ant), Camponotus pennsylvanicus (DeGeer) (The Black or Eastern Carpenter Ant) and Camponotus novaeboracensis (Fitch) (The New York Carpenter Ant)) to pass through. At the end of the search time, I took the mesh basket out of the white bucket, collected any ants in the bucket, shook the material in the mesh basket over a white drop-cloth until a thin layer of material covered the cloth, and collected any ants that had fallen onto the cloth. I repeated this process of shaking the mesh basket and collecting ants three times per litter sample, and before each shaking event I mixed the litter by hand and broke apart sticks and stems when they were present. Ants collected through this process were kept separate from ants collected through the timed visual searches.

    Baiting. I used limited bait sampling at most of the study plots during both years, but these data were excluded from the analyses due to baits being strongly biased toward some species. No species were collected through bait sampling that were not also collected by other methods during the study. However, within individual plots, bait sampling did occasionally yield the only record of a species for that plot.

    Environmental measurements

    I measured vegetation structure along each transect, at 24 evenly spaced points (5.5-m spacing) in 2012, and 30 evenly spaced points (4-m spacing) in 2013. At each sampling point, I estimated the vegetation cover within three height classes (under 0.5 m, between 0.5 and 1 m, and between 1 m and 2 m) by recording whether or not vegetation contacted a 2.7-cm diameter pole placed vertically on the ground. I also recorded whether there was vegetation present above the 2 m pole. I recorded the proportion of sampling points on each transect at which specific types of vegetation (grasses/sedges and shrub-level oaks) contacted the pole. Finally, I classified the ground cover at the base of the pole as “bare”, “green” or “dead”, based on whether the ground within 20 cm of the base of the pole was primarily bare soil (bare), living plant material (green), or dead plant material (dead). Although “bare” was mutually exclusive of the other two classes, the area surrounding the pole could be covered by both living material and dead material, as was often the case when living plants were growing above a layer of leaf litter.

    I used a spherical densiometer (Robert E. Lemmon, Forest Densiometers, Model-A) to estimate the percent cover of vegetation around each ant-sampling quadrat. Four densiometer readings were taken at each quadrat, one facing outward from each side of the quadrat, approximately 0.5 m above the ground. Finally, I measured the litter depth in the center of each quadrat to the nearest full centimeter beneath the litter surface.

    Soil type and plot area were not measured at the time of the ant surveys. I obtained soil data for each of my study plots from the online database websoilsurvey.sc.egov.usda.gov (SSSNRCS 2014), but did not include it as a possible variable explaining variation in ant assemblages due to the data being unreplicated categorical variables. I defined the area of each study plots as the extent of contiguous, open, barrens habitat, and estimated the total area of these plots by drawing polygons over satellite images with the software Google Earth Pro (version 7.1.2.2041).

    Habitat classification

    Much of the APBP has open habitat dominated by shrub-level oaks, whereas open habitats at SSP and RSP tend to be dominated by graminoids. The APBPC defines the shrub-covered areas of the preserve as either Pitch Pine-Scrub Oak barrens if the shrub-level oaks constitute 30%-60% cover, or as Pitch Pine-Scrub Oak thicket if the shrub-level oaks cover >60% of the ground area. The APBPC is seeking to increase the percentage of the preserve falling into these habitat types (APBPC 2010), but Pitch Pine-Scrub Oak barrens is preferred over Pitch Pine-Scrub Oak thicket, because it allows for the persistence of Lupinus angustifolius L. (Wild Blue Lupine) and the Karner Blue Butterfly (Bried and Gifford 2010).

    I classified the plots as either grassland or shrubland based on the percentage of my sampling points at which shrub-level oaks intersected the point (i.e. contacted the pole). Any plot in which at least 30% of the sampling points were intersected by shrub-level oaks was classified as shrubland, and the plots that had less than this percentage were classified as grassland (in all plots, graminoids intersected at least 50% of the sampling points). The shrubland plots included four of the six plots from 2013. One of the grassland plots (GD) from 2013 had been restored from a woodland in 2008, and another (DP) was burned in 2011. The latter had a strong shrub-level oak component, but the plants were small at the time of the survey, resulting in just 13% cover by my measurements.

    Specimen identification

    I identified the ant specimens, relying almost exclusively on the dichotomous keys in Ellison et al. (2012), and aided by data and images from Antweb.com. A subset of the ants were pinned and identifications confirmed by Aaron M. Ellison (Harvard Forest, Petersham, MA). Identifications of rare and particularly challenging specimens were confirmed by Stefan Cover at the Museum of Comparative Zoology in Cambridge, MA. Voucher sets have been sent to the Albany State Museum, Albany, NY, and the Museum of Comparative Zoology in Cambridge, MA.

    Distinguishing specimens of Aphaenogaster rudis Enzmann (The Rough Aphaenogaster) and a closely related species Aphaenogaster picea (Wheeler) (The Pitch-black Aphaenogaster) is difficult and these are some of the most abundant species in eastern woodlands. Due to my uncertainty in identification of these common ants, I lumped all of the specimens of these species under the name of the more common species, A. picea, for the analyses described below. However, one specimen from a study plot in the APBP was positively identified as A. rudis by Bernice DeMarco (Department of Entomology, Michigan State University, East Lansing, MI), so both species appear in the species list for the APBP.

    Data analysis

    For information on EstimateS, please see the users guide: http://viceroy.eeb.uconn.edu/estimates/EstimateSPages/EstSUsersGuide/EstimateSUsersGuide.htm#WhatEstimateSComputes.

    Literature Cited

    Agosti, D., J. D. Majer, L. E. Alonso, and T. R. Schultz, editors. 2000. Ants: standard methods for measuring and monitoring biodiversity. Smithsonian Institution Press.

    Albany Pine Bush Preserve Commission (APBPC), 2010. Management Plan/Final Environmental Impact Statement for The Albany Pine Bush Preserve. Albany Pine Bush Preserve Commission Technical report.

    Barnes, J. K. 2003. Natural history of the Albany Pine Bush. New York State Museum, Albany, NY.

    Bried, J. T., and N. A. Gifford. 2010. Mowing and herbicide of scrub oaks in pine barrens: baseline data (New York). Ecological Restoration 28:245–248.

    Ellison, A. M., N. J. Gotelli, E. J. Farnsworth, and G. D. Alpert. 2012. A Field Guide to the Ants of New England. 1st edition. Yale University Press, New Haven, CT.

    Greenslade, P.J.M. 1973. Sampling ants with pitfall traps: Diggin-in effects. Insectes Sociaux 20:343–353.

    Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture (SSSNRCS). 2013. Web Soil Survey. Available online at http://websoilsurvey.nrcs.usda.gov/. Accessed 10 April 2014.

  • Use:

    This dataset is released to the public under Creative Commons license CC BY (Attribution). Please keep the designated contact person informed of any plans to use the dataset. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the dataset must include proper acknowledgement.

  • Citation:

    Barber G, Ellison A. 2015. Ants in the New York Pine Barrens 2012-2013. Harvard Forest Data Archive: HF239.

Detailed Metadata

hf239-01: ants

  1. state: state
    • NY: New York
  2. county: county
  3. location: locality abbreviation
    • APBP: Albany Pine Bush Preserve
    • SSP: Saratoga Sand Plains
    • RSP: Rome Sand Plains
  4. plot.name: full name of study plot
  5. plot: abbreviations of study plot names. These are only given for plots that were included in the published manuscript.
  6. habitat: habitat type based on measurements of shrub-level oak frequency
    • Grassland: <30% cover by shrub-level oaks
    • Shrubland: ≥30% cover by shrub-level oaks
  7. latitude: latitude (unit: degree / missing value: NA)
  8. longitude: longitude (unit: degree / missing value: NA)
  9. coll.date: collection date
  10. method: collection method
    • 1 h timed search: searched the study plot for one hour
    • 2nd HC .5 h: a second half-hour timed search of the study plot
    • Bucket Capture: ants that were inadvertently knocked off of vegetation and into a bucket as it was carried through a plot
    • Extra Hand Collection: ants that were observed and collected, but not as part of the official study design
    • HC .5 h: the first half-hour timed search of the study plot
    • NB-AA: amino acid bait
    • NB-HD: honey dew bait
    • NB-OO: olive oil bait
    • NB-SU: sugar-water bait
    • NB-WA: water bait
    • Nest Collection: deliberate collection of a nest, including the attempted collection of the queen
    • Notes: indicates that the row holds field notes, not a record of ants collected
    • Pecan Sandies Bait: Pecan Sandies cookie bait
    • Pitfall Trap: pitfall trap
    • Quadrat Search: 1-m quadrat search
    • Quadrat Search.pilot: pilot studies of the quadrat search method
  11. sampling.unit: number of the sampling unit (i.e. the pitfall trap or quadrat number)
  12. time: approximate time of day at which the specimen was collected
  13. coll.before: secondary time data. If no time was recorded for the specimen, it was at least collected before this time.
  14. subhabitat: position relative to the hiking trail
    • i: interior (>35 m from any habitat edge)
    • e: edge (~10 – 60 cm from hiking trail)
    • t: hiking trail
    • Path/Interior: path/interior
  15. survey: surveys conducted in 2013
    • 1: first survey
    • 2: second survey
    • NA: not applicable because there was only a single survey conducted in that year or for that method
  16. vial: vial identification number
  17. subfamily: subfamily
  18. genus: genus
  19. species: species
  20. authority: taxonomic authority
  21. code: species code. Typically, the first three letters of the genus followed by the first four letters of the species name.
  22. caste: ant caste
    • B: brood
    • D: drone/male reproductive
    • M: major
    • Q: queen
    • W: worker
  23. num: number of ants (unit: number / missing value: NA)
  24. type: type of ant observation. These data were typically collected during quadrat searches alone.
    • C: directly collected from a colony
    • S: collected as a stray, not directly from a colony
    • L: collected from litter sifting
  25. id.by: the most recent individual who identified the specimen
    • AMD: Amanda Dillon
    • AME: Aaron Ellison
    • BBD: Bernice DeMarco
    • GWB: Grace Barber
    • SPC: Stefan Cover
  26. id.date: when recorded, the data on which the species identification was made
  27. coll.by: the individual who collected the specimen
    • A. M. Dillon: Amanda Dillon
    • G. W. Barber: Grace Barber
  28. num.pinned: the number of pinned specimens (unit: number / missing value: NA)
  29. mcz: these specimens are in the collection in the Museum of Comparative Zoology in Cambridge, MA (unit: number / missing value: NA)

hf239-02: environment

  1. location: locality abbreviation
    • APBP: Albany Pine Bush Preserve
    • SSP: Saratoga Sand Plains
    • RSP: Rome Sand Plains
  2. plot.name: full name of study plot
  3. plot: abbreviations of study plot names
  4. year: year surveyed
  5. open: number of contiguous, open habitat surrounding the study transects (unit: hectare / missing value: NA)
  6. habitat: habitat type based on measurements of shrub-level oak frequency
    • Grassland: <30% cover by shrub-level oaks
    • Shrubland: ≥30% cover by shrub-level oaks
  7. latitude: latitude (unit: degree / missing value: NA)
  8. longitude: longitude (unit: degree / missing value: NA)
  9. veg.intersect: median number of intersections of vegetation at sampling points within 0.5 m of the ground surface. This can be thought of as a measure of the density of vegetation below 0.5 m. (unit: number / missing value: NA)
  10. bare.per: the percent of sampling points at which the ground was primarily bare soil within 20 cm of the sampling point (unit: dimensionless / missing value: NA)
  11. green.per: percent of sampling points at which a majority of the ground within 20 cm of the point was covered by living plant material (unit: dimensionless / missing value: NA)
  12. dead.per: percent of sampling points at which a majority of the ground within 20 cm of the point was covered by dead plant material (unit: dimensionless / missing value: NA)
  13. veg0.5.per: percent of sampling points at which vegetation occurred between 0.5 and 1 m of the ground (unit: dimensionless / missing value: NA)
  14. veg1.0.per: percent of sampling points at which vegetation occurred between 1 and 2 m of the ground (unit: dimensionless / missing value: NA)
  15. veg2.0.per: percent of sampling points at which vegetation occurred above 2 m from the ground (unit: dimensionless / missing value: NA)
  16. scrub.oak.per: percent of the sampling points that were intersected by scrub-level oaks (unit: dimensionless / missing value: NA)
  17. graminoid.per: percent of the sampling points that were intersected by graminoids (unit: dimensionless / missing value: NA)
  18. litter.med: median litter depth among quadrats (unit: dimensionless / missing value: NA)
  19. bare.med: median percentage of bare ground among quadrats (unit: dimensionless / missing value: NA)
  20. cover.med: median percent overstory cover among quadrats (unit: dimensionless / missing value: NA)
  21. cover.var: variance in percent overstory cover among quadrats (unit: dimensionless / missing value: NA)

hf239-03: estimate-s output

  1. cv.abun.dist: an integer giving the coefficient of variation (CV) of the abundance distribution, if the CV was >0.5. If the CV was <0.5, the word “good” is entered, indicating that the bias-corrected formula should be used.
  2. cv.inci.dist: an integer giving the coefficient of variation (CV) of the incidence distribution, if the CV was >0.5. If the CV was <0.5, the word “good” is entered, indicating that the bias-corrected formula should be used.
  3. formula: indicates whether the value is for the bias-corrected or classic formulas
    • B: Bias-corrected formula
    • C: Classic formula
  4. plot: plot
    • AR: Apollo Restoration
    • BF: Baron's Field
    • BH: Blueberry Hill West
    • CS: Camp Saratoga
    • DC: Discovery Center Field
    • DP: Draperies
    • GD: Great Dune
    • KB: King's Road Barrens
    • KE: Karner Barrens East
    • KW: Karner Barrens West
    • RS: Rome Sand Plains Field
    • TR: Trinity
  5. samples: number of sampling units accumulated (unit: number / missing value: NA)
  6. individuals: [t/T]*N, where T is the number of sampling units in the reference sample and N is the total number of individuals in all T samples (makes sense for sample-based abundance date only) (unit: number / missing value: NA)
  7. s.est: expected number of species in t pooled samples, given the reference sample (analytical) (unit: number / missing value: NA)
  8. s.est.95ci.lower.bound: lower bound of 95% Confidence Interval for S(est) (unit: dimensionless / missing value: NA)
  9. s.est.95ci.upper.bound: upper bound of 95% Confidence Interval for S(est) (unit: dimensionless / missing value: NA)
  10. s.est.sd: standard deviation of S(est) (analytical) (SD = SE) (unit: dimensionless / missing value: NA)
  11. s.mean: number of species in t pooled samples, given the reference sample (mean among runs) (unit: dimensionless / missing value: NA)
  12. singletons.mean: number of singletons (species with only one individual) in t pooled samples or among m individuals (mean among runs) (unit: number / missing value: NA)
  13. singletons.sd: standard deviation of Singletons, among randomizations of sample order (unit: dimensionless / missing value: NA)
  14. doubletons.mean: number of doubletons (species with only two individuals) in t pooled samples or among m individuals (mean among runs) (unit: number / missing value: NA)
  15. doubletons.sd: standard deviation of doubletons, among randomizations of sample order (unit: dimensionless / missing value: NA)
  16. uniques.mean: number of uniques (species that occur in a only one sample) in t pooled samples (mean among runs) (unit: number / missing value: NA)
  17. uniques.sd: standard deviation of Uniques, among randomizations of sample order (unit: dimensionless / missing value: NA)
  18. duplicates.mean: number of duplicates (species that occur in a only two samples) in t pooled samples (mean among runs) (unit: number / missing value: NA)
  19. duplicates.sd: standard deviation of duplicates, among randomizations of sample order (unit: dimensionless / missing value: NA)
  20. ace.mean: abundance coverage-based estimator of species richness (mean among runs) (unit: dimensionless / missing value: NA)
  21. ace.sd: Standard deviation of ACE, among randomizations of sample order or individual order (unit: dimensionless / missing value: NA)
  22. ice.mean: incidence coverage-based estimator of species richness (mean among runs) (unit: dimensionless / missing value: NA)
  23. ice.sd: standard deviation of ICE, among randomizations of sample order (unit: dimensionless / missing value: NA)
  24. chao1.mean: chao 1 richness estimator (mean among runs) (unit: dimensionless / missing value: NA)
  25. chao1.95ci.lower.bound: chao 1 log-linear confidence interval lower bound (mean among runs) (unit: dimensionless / missing value: NA)
  26. chao1.95ci.upper.bound: chao 1 log-linear confidence interval upper bound (mean among runs) (unit: dimensionless / missing value: NA)
  27. chao1.sd: chao 1 standard deviation (by Chao's formulas) (unit: dimensionless / missing value: NA)
  28. chao2.mean: chao 2 richness estimator (mean among runs) (unit: dimensionless / missing value: NA)
  29. chao2.95ci.lower.bound: chao 2 log-linear confidence interval lower bound (mean among runs) (unit: dimensionless / missing value: NA)
  30. chao2.95ci.upper.bound: chao 2 log-linear confidence interval upper bound (mean among runs) (unit: dimensionless / missing value: NA)
  31. chao2.sd: chao 2 standard deviation (by Chao's formula) (unit: dimensionless / missing value: NA)
  32. jack.1.mean: first-order Jackknife richness estimator (mean among runs) (unit: dimensionless / missing value: NA)
  33. jack1.sd: first-order Jackknife standard deviation (unit: dimensionless / missing value: NA)
  34. jack2.mean: second-order Jackknife richness estimator (mean among runs) (unit: dimensionless / missing value: NA)
  35. jack2.sd: standard deviation of Jack2, among randomizations of sample order (unit: dimensionless / missing value: NA)
  36. bootstrap.mean: bootstrap richness estimator (mean among runs) (unit: dimensionless / missing value: NA)
  37. bootstrap.sd: standard deviation of Bootstrap, among randomizations of sample order (unit: dimensionless / missing value: NA)
  38. mmruns.mean: michaelis-Menten richness estimator: estimators averaged over randomizations (mean among runs) (unit: dimensionless / missing value: NA)
  39. mmmeans: michaelis-Menten richness estimator: estimators computed once for analytica rarefaction curve, computed by Eq. 5 in Colwell et al. (2004) (unit: dimensionless / missing value: NA)
  40. cole.rarefaction: coleman rarefaction (number of species expected in t pooled samples, assuming individuals distributed at random among samples) (unit: number / missing value: NA)
  41. cole.sd: coleman standard deviation (analytical) (unit: dimensionless / missing value: NA)
  42. alpha.mean: fisher's alpha diversity index (unit: dimensionless / missing value: NA)
  43. alpha.sd: fisher's alpha standard deviation (unit: dimensionless / missing value: NA)
  44. shannon.mean: shannon diversity index (mean among runs), natural logarithms (unit: dimensionless / missing value: NA)
  45. shannon.sd: standard deviation of Shannon index among randomizations of sample order (unit: dimensionless / missing value: NA)
  46. shannon.exp.mean: exponential Shannon diversity index (mean among runs) (unit: dimensionless / missing value: NA)
  47. shannon.exp.sd: standard deviation of Exponential Shannon index among randomizations of sample order (unit: dimensionless / missing value: NA)
  48. simpson.inv.mean: simpson (inverse) diversity index (mean among runs) (unit: dimensionless / missing value: NA)
  49. simpson.inv.sd: standard deviation of Simpson (inverse) index among randomizations of sample order (unit: dimensionless / missing value: NA)

hf239-04: photo and map

  • Compression: none
  • Format: pdf
  • Type: pdf file