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2015 NORTHEASTERN NATURALIST 22(3):551–572
Ant Assemblages of New York State Inland Pine Barrens
Grace W. Barber*
Abstract - Ants are major contributors to ecological processes including soil development,
nutrient cycling, and seed dispersal in the northeastern US and around the world. However,
distributions of these influential invertebrates in the inland Pitch Pine barrens of New York
State are poorly understood. I used quadrat searches and pitfall traps to systematically
sample ant assemblages along transects in open habitats at 3 of these inland barrens. My
results demonstrate that (1) inland Pitch Pine barrens in New York support high ant-species
density, including rare species; (2) as in other regions, shrubland habitats appear to support
higher ant-species density than grassland habitats in the Northeast; and (3) shrubland and
grassland ant-assemblages in these barrens are compositionally distinct.
Introduction
Ants are important contributors to ecosystem function in most terrestrial environments.
In the northeastern US, ants contribute substantially to nutrient cycling
and decomposition and are among the most important seed dispersers and soil
developers in woodlands and likely in other habitat types where they are found.
(Del Toro et al. 2012, Folgarait 1998, Frouz and Jilková 2008, Handel et al. 1981,
Lyford 1963). Although the ecological importance and potential utility of ants for
ecosystem monitoring are widely accepted (Andersen and Majer 2004, Ellison
2012b), ant assemblages of many ecosystems, including some of high conservation
concern, have been surveyed only rarely or, in some cases, never. It is important to
monitor and study these assemblages to enhance ecological knowledge and because
they are susceptible to dramatic change following invasion by non-native ant species,
changes in habitat, or changes in disturbance regimes, which can then alter
ant-mediated ecosystem processes (Christian 2001, Rodriguez-Cabal et al. 2012).
Although Pinus sp. (pine) barrens ecosystems are well known for their invertebrate
diversity (Barnes 2003, Wagner et al. 2003, Wheeler 1991), most have not
been thoroughly or recently surveyed for ants. Evidence from previous surveys in
both inland and coastal pine barrens (Barnes 2003, Dindal 1979, Ellison 2012a)
suggests that these ecosystems may have high ant-species richness relative to other
habitat types. Barnes (2003) published a species list of ants from the Albany Pine
Bush Preserve (APBP) that included 32 species (after updates to the taxonomy),
but the data available from the APBP on which this list was based did not include a
number of large areas and many habitat types occurring on the APBP, nor have ant
assemblages been studied at other inland barrens systems in New York State.
Inland Pinus rigida Mill. (Pitch Pine) barrens are characterized by well-drained
sandy soil, an open canopy of Pitch Pine, variable shrub layers, and grassy patches
*Department of Environmental Conservation, University of Massachusetts, Amherst, MA
01003; gracebarber.w@gmail.com.
Manuscript Editor: Joshua Ness
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(NatureServe 2014). Within inland pine barrens throughout the Northeast, there is
variability in the presence and density of shrub-level Quercus (oak) species such as
Quercus ilicifolia (Wangenh.) (Scrub Oak/Bear Oak) and Quercus prinoides (Willd.)
(Dwarf Chinquapin Oak). The encroachment of woody plants into grasslands is a
phenomenon that has received considerable attention in recent years (Eldridge et
al. 2011, Quero et al. 2013), and other authors have shown that this ecosystem
change can increase ant-species density in other arid and mesic environments in
the southwestern US and in Europe (Bestelmeyer 2005, Kumschick et al. 2009,
Wiezik et al. 2013). In the northeastern US, however, the difference in ant-species
density between shrublands and grasslands is not known. At northern latitudes in
North America, ant richness has been observed to decrease with increasing canopy
cover (Del Toro et al. 2013, Gotelli and Arnett 2000, Jeanne 1979, Ouellette et al.
2010), and other authors have suggested this phenomenon is likely due to the cooler
temperatures of shaded areas being thermally limiting to some species (Banschbach
and Ogilvy 2014, Del Toro et al. 2013). If this explanation is true, oak-dominated
shrublands might be expected to have lower ant-species density than grasslands in
northern latitudes, owing to the greater shading-capacity of broad-leaved plants.
I surveyed ant assemblages in 2 barrens habitat types among 3 inland Pitch Pine
barrens systems: grasslands and shrublands at APBP, and grasslands at the Saratoga
Sand Plains (SSP) and the Rome Sand Plains (RSP). Of these 3 preserves, APBP
is the only one where managers actively create shrubland habitat. The objectives
of my study were to (1) create or update ant-species lists for 3 inland pine-barrens
preserves, (2) assess the relative ant-species richness of pine barrens compared to
other habitat types in the region, and (3) identify environmental variables that best
explain patterns in ant-assemblage composition and species density among these
barrens habitats.
My comparison of ant-species density among shrubland and grassland habitats of
these inland pine barrens provides a case study from the northeastern US that examines
the effect of shrub density in open habitats on ant assemblages. Results from my survey
of these inland-barrens ecosystems and comparison of shrubland and grassland ant assemblages
within the barrens provide new knowledge of ant-species distributions and
diversity. This knowledge will improve our ability to monitor ant assemblages and develop
appropriate conservation strategies to promote ant biodiversity.
Field-site Description
The APBP is located between 42.67 and 42.76°N latitude and 73.82 and 73.94°W
longitude at an elevation of ~100 m above sea level (masl) in Albany County, NY
(Fig. 1), between the cities of Albany and Schenectady. The average annual temperature
for the city of Albany is 9.7 °C, and the average annual precipitation is 875
mm (CantyMedia 2014). The preserve is comprised of 1295 ha of protected land and
several habitat types, including mixed deciduous and conifer forests, Pitch Pine–
Scrub Oak barrens, open grasslands, Scrub Oak thickets, and wetlands. Albany is
one of the oldest cities in the US, and the region that includes the APBP has been
a center of commerce and travel for well over 200 years. The land included in the
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preserve has a history of agriculture and excavation of sand for glass-making and
molding sands. Today, the APBP is maintained by the Albany Pine Bush Preserve
Commission (APBPC), which restores and maintains the Pitch Pine–Scrub Oak
barrens community through a combination of mowing, herbicide application, tree
removal, planting, and prescribed burns (APBPC 2010, Bried and Gifford 2010).
The SSP is located between 43.13 and 43.16°N latitude and 73.72 and 73.68°W
longitude, ~50 km north of the APBP, at an elevation of ~90 masl in Saratoga
County, NY, ~9.6 km north of the city of Saratoga Springs. The average annual
temperature for Saratoga Springs is 8.9 °C, and the average annual precipitation
is 1143 mm (CantyMedia 2014). Most of the protected land is part of the Wilton
Wildlife Preserve, which includes ~280 ha of wetlands and oak–pine forests and
savannas (NYSDEC 2014). The SSP lands are currently monitored and maintained
Figure 1. Maps of study sites and plots. The locations of the study sites are indicated by
their abbreviations (RSP, SSP, and APBP) in the map in the upper left panel. The other 3
panels show study-plot locations (indicated by their abbreviations; see Table 2 for plot
codes) within the study sites.
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by the Nature Conservancy, the New York State Department of Environmental Conservation
(DEC), and The Wilton Wildlife Preserve and Park through a combination
of vegetation clearing, mowing, and planting.
The RSP is located between 43.22 and 43.24°N latitude and 75.56 and 75.58°W
longitude at an elevation of ~130 masl in Oneida County, NY, ~6.4 km west of the
city of Rome. RSP is separated from SSP and APBP by a distance of ~150 km. The
average annual temperature for the city of Rome is 8 °C, and the average annual
precipitation is 1082 mm (CantyMedia 2014). The RSP includes an area of ~6475
ha, but much of it has been developed by private landowners. The DEC, Oneida
County, The Nature Conservancy, and the Izaak Walton League each own sections
of undeveloped land within the RSP system, and their combined holdings amount
to ~1568 ha dominated by mixed northern hardwood and pine forests, wetlands,
dunes, and occasional open grasslands. A plan to manage the open grasslands has
been written that recommends conducting vegetative management to maintain the
Pitch Pine–heath barrens community; however, little restoration and management
work was underway at RSP at the time of this study (RSPRMT 2006).
Methods
Study plots
I surveyed 6 plots in 2012 and 6 in 2013. The 2012 plots were divided among
the 3 field sites—3 in the APBP, 2 in the SSP, and 1 in the RSP—and all were in
areas of relatively homogenous vegetation that ranged in size from 1.8 to 17.1
contiguous hectares (Table 1). I refer to the 2012 APBP plots as Discovery Center
Field (DC), Apollo Restoration (AR), and Baron’s Field (BF); the 2 at SSP as Camp
Saratoga (CS) and Trinity (TR), and the sole plot at RSP as Rome Sand Plains Field
(RS). To maximize habitat similarity of plots across the 3 pine-barrens systems, I
selected flat, open areas dominated by graminoids and heaths, with little or no cover
of shrub-level oaks. Location, size, and vegetation-cover data for these plots are
presented in Table 1.
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 overstory of Pitch Pine and
oak species (Table 1). All of the 2013 plots were located adjacent to hiking trails.
All but 1 (KE) of the 2013 plots contained a substantial dune and correspondingly
steep topography over portions of the plot.
The soil underlying most of the study plots at the 3 sites was loamy fine sand that
was well to excessively drained, rapidly permeable, characterized by strong to medium
acidity, and devoid of gravel (Barnes 2003, SSSNRCS 2014). However, the 3
APBP plots surveyed in 2012 were located on areas that had been heavily impacted
by human activity, and the soils at these sites were classified as Udipsamments (AR
and DC) and Udorthents (BF) (SSSNRCS 2014). The Udipsamments of AR and DC
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differed from soils at all other plots in that the top layer was coarse sand rather than
loam or loamy fine sand. The Udorthents soil of BF had an upper layer of loam,
which was similar to that of most other plots, but which was 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) (Table 1).
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 <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 I randomly determined the exact placement of the transect (or first transect
segment, from which all others were based). 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 set a pitfall trap at the midpoint between each of the quadrats.
In 2013, I placed a single 120-m transect within each plot aligned roughly parallel
to the trail that cut through the plot and 35–45 m from the trail. These transects
were at least this far from any of the other habitat edges, except at their ends,
which I allowed to be as close as 20 m from the habitat edge when necessary. As in
2012, I randomly determined the exact placement of the transect, placed ten 1-m2
quadrats along the transect at 11-m intervals, and randomly determined the distance
from 1 end of the transect to the first quadrat. I surveyed each of the 2013 transects
Table 1. Study-plot characteristics. Locality (loc.): RSP = Rome Sand Plains, SSP = Saratoga Sand
Plains, and APBP = Albany Pine Bush Preserve. Habitat (Hab.): Gr = grassland, Sh = shrubland. The
texture of the upper 18 cm of soil: CS = coarse sand, L = loam, and LFS = loamy fine sand. Drainage
class (class): W = well drained, M = moderately well drained, S = somewhat excessively drained, and
E = excessively drained. Incidence of graminoids and shrub-level oaks refers to their occurrence at
sampling points along transects. I determined median cover with a densiometer.
Incidence (%) of
Plot shrub- Median Texture
size gram- level canopy upper
Loc. Plot Lat (°N), long (°W) (ha) Hab. inoids oaks cover 18 cm Class
RSP RSP field 43.23065, 75.57895 1.8 Gr 87.5 0.0 0.0 LFS E
SSP Camp Saratoga 43.15621, 73.69557 5.6 Gr 83.3 0.0 4.4 LFS W
SSP Trinity 43.16033, 73.70377 2.1 Gr 87.0 0.0 2.0 LFS W
APBP Apollo Restoration 42.72235, 73.86834 1.8 Gr 65.2 4.3 0.4 CS W
APBP Barons Field 42.73772, 73.89195 6.2 Gr 54.5 9.1 7.2 L, LFS M
APBP DC Field 42.71952, 73.86365 3.7 Gr 73.9 0.0 4.7 CS W
APBP Draperies 42.71830, 73.88420 4.4 Gr 60.0 13.3 21.3 LFS S
APBP Great Dune 42.70600, 73.89779 17.1 Gr 53.3 0.0 16.0 LFS S
APBP Blueberry Hill West 42.70064, 73.86961 18.5 Sh 53.3 50.0 65.3 LFS S
APBP Kings Road Barrens 42.72390, 73.87666 9.7 Sh 90.0 53.3 51.5 LFS S
APBP Karner Barrens East 42.71402, 73.86525 6.8 Sh 80.0 70.0 38.7 LFS S
APBP Karner Barrens West 42.71904, 73.87059 11.3 Sh 93.3 53.3 31.5 LFS W
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twice: once in May–June, and again in July–August. I randomized the order of plot
sampling during both surveys. I offset the 10 quadrats sampled along each transect
during the second survey from the 10 sampled during the first survey by a distance
of 1 m (a full meter between the 2 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 3 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, 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 (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 (1 transect per plot) for 15 min; in 2013 I used 8-min searches for ants
in the twenty 1-m2 quadrats per transect (1 transect per plot). I conducted the 2013
quadrat searches over 2 survey periods so that I could search 10 quadrats per plot
during each of the 2 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. I did not include in search time the periods spent recording and transferring
specimens. I completed visual searches and pitfall trapping during dry weather and
did not employ the 2 methods 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. Conversely, the
2012 plots 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 plots with no litter or litter made up
of dead grass, which meant that I was more likely to overlook ants in the quadrats
beneath shrub-level oaks than in other sites. 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
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bucket. The mesh holes were parallelograms with corner -to-corner distances of 30
and 50 mm, which was large enough for the largest species I encountered—Camponotus
americanus Mayr (American Carpenter Ant), Camponotus pennsylvanicus
(DeGeer) (Black or Eastern Carpenter Ant), and Camponotus novaeboracensis
(Fitch) (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 3 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. I kept the ants collected with this process
separate from ants collected during the timed visual searches. Table 2 provides a
list of the methods used in 2012 and 2013.
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 3 height classes (<0.5
m, ≥0.5–1 m, and >1 m–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 groundcover 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),
Table 2. Ant sampling at the 12 study plots. The Methods column provides a list of the methods used
to collect ant specimens at the given plot. QS = timed quadrat search, PT = pitfall trap, and LS =
litter sample. Note that there are different lengths of time given for the quadrat searches, and litter
samples in 2013 were part of the protocol for the timed quadrat search. The observed richness is the
total number of ant species collected through the study methods listed and includes both surveys in
2013. Conversely, the Chao 2 mean richness (SD) is based on only the first survey for the 2013 plots
and the quadrat searches from 2012; it is the estimated species density in 10 m2, as determined by the
Chao2 formula.
Observed Chao2 mean
Year Plot Habitat Methods richness richness (SD)
2012 AR: Apollo Restoration Grassland 15-min QS; PT 6 6 (2)
2012 BF: Barons Field Grassland 15-min QS; PT 13 17 (8)
2012 DC: DC Field Grassland 15-min QS; PT 9 12 (5)
2012 RS: RSP Field Grassland 15-min QS; PT 14 15 (2)
2012 CS: Camp Saratoga Grassland 15-min QS; PT 11 11 (4)
2012 TR: Trinity Grassland 15-min QS; PT 19 20 (10)
2013 DP: Draperies Grassland 8-min QS; LS 20 17 (1)
2013 GD: Great Dune Grassland 8-min QS; LS 12 12 (3)
2013 BH: Blueberry Hill West Shrubland 8-min QS; LS 23 34 (13)
2013 KB: Kings Road Barrens Shrubland 8-min QS; LS 28 46 (18)
2013 KE: Karner Barrens East Shrubland 8-min QS; LS 23 24 (8)
2013 KW: Karner Barrens West Shrubland 8-min QS; LS 19 28 (12)
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or dead plant material (dead). Although bare was mutually exclusive of the other 2
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 (R.E. Lemmon, Forest Densiometers, Model-A)
to estimate the percent cover of vegetation around each ant-sampling quadrat. I
took 4 densiometer readings in each quadrat: 1 facing outward from each side of the
quadrat, ~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.
I did not measure soil type or plot area 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’s classification as an unreplicated
categorical variable. I defined the area of each study plot 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 P ro (Google 2014).
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 managers favor Pitch Pine–Scrub Oak barrens 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 Gif ford 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). I classified any plot in which at least 30% of the sampling points were
intersected by shrub-level oaks as shrubland, and the plots that had less than this
percentage as grassland (in all plots, graminoids intersected at least 50% of the
sampling points). The shrubland plots included 4 of the 6 plots from 2013. One of
the grassland plots (GD) from 2013 had been restored from woodland habitat 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 (Table 1).
Specimen identification
I identified the ant specimens, relying almost exclusively on the dichotomous
keys in Ellison et al. (2012) aided by data and images from AntWeb (2015). I pinned a
subset of the ants, and A.M. Ellison (Harvard Forest, Petersham, MA) confirmed my
identifications. S. Cover ( Museum of Comparative Zoology, Cambridge, MA) confirmed
identifications of rare and particularly challenging specimens. I sent a set of
voucher specimens to the Museum of Comparative Zoology.
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Distinguishing specimens of Aphaenogaster rudis (Rough Aphaenogaster) and a
closely related species Aphaenogaster picea (Pitch-black Aphaenogaster) was difficult.
These are some of the most abundant species in eastern woodlands. Due to
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, when analyzing
the data. However, 1 specimen from a study plot in the APBP was positively
identified as A. rudis by B. DeMarco (Department of Entomology, Michigan State
University, East Lansing, MI), so both species appear in the species list for the
APBP (Table 3).
Data analysis
Interpreting ant numbers. Many authors have emphasized the importance of
not basing relative species-abundance estimates on the numbers of individual
ants, but rather on colony numbers (reviewed by Gotelli et al. 2011). For plotlevel
comparisons, I used the instances of species occurrence (termed species
incidence) in distinct sub-samples (i.e., quadrats or pitfall traps), which I consider
a valid surrogate for colony frequency, rather than worker numbers or direct
colony observations alone. This decision was supported by a highly significant
correlation in the rank order of the overall incidences of detection and the incidences
of colony detection for each species (Spearman’s rho) (ρ = 0.87, P <
0.0005; Fig. 2). Species-specific differences in nest structure (i.e., polydomy vs.
monodomy), which influence probability of nest detection, and the fact that I collected
1 species (Monomorium emarginatum [Furrowed Monomorium]) more
readily in pitfall traps and always counted them as strays when I collected by this
method, likely contributed to the variance in this relationship.
Species density. Species density is defined as the number of species per unit area,
whereas species richness is the total number of species in a habitat or ecosystem.
Samples in the current study were area-based; thus, they can be used to estimate the
species density of ants in my study plots (Gotelli and Colwell 2001). I compared
species density across plots by rarefying my data to adjust for unequal sample
sizes and examining the species-accumulation curves and estimates of the true
species density of the plots and of the habitat types. I used the software EstimateS
(Colwell 2013) to rarefy the data and generate the Chao2 estimates of true density.
The Chao2 formula is recommended for making comparisons across samples for
incidence-based data (Chao et al. 2014) and provides more reliable estimates of the
true species density by taking into account the completeness of the sample based on
the number of species recorded only once or twice in the sample.
To more reliably compare species density across plots, I minimized the effect of
sampling method by using only the data from ants collected during quadrat searches.
Furthermore, I used only the first survey from the 2013 data because the quadrats from
the first and second surveys did not necessarily sample independent colonies due to
their spatial proximity. I decided to use the first rather than the second survey from
2013 because the first survey had a higher degree of seasonal overlap with the 2012
survey. Thus, I included in the analysis all twelve 1-m2 quadrat searches for each of the
2012 plots and ten 1-m2 quadrat searches per plot from the 2013 data. I then compared
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the estimates of rarefied species density for 10 quadrats per plot, the largest number
for which Chao2 estimates could be calculated for all plots.
In 9 of the 12 sites (BF, BH, DC, GD, KB, KE, and KW at APBP; CS and TR
at SSP), the coefficient of variation of the incidence distribution was >0.5. Under
these circumstances, it is recommended to use the larger of the Chao2 estimates calculated
using either the bias-corrected or classic formula (Colwell 2013). In every
case, the estimate from the classic formula was larger for these sites, so I used that
estimate in all analyses involving species density. I used the bias-corrected formula
for the other 3 sites (AR, DP, and RS).
Compositional differences. I used redundancy analysis (RDA) to identify patterns
in ant-assemblage composition across my sites and simultaneously identify
the measured environmental variables most closely correlated with differences in
assemblage composition (Gotelli and Ellison 2012). The data used in this analysis
were ant-species incidences (occurrence in pitfall traps or quadrats) for each
plot, and included only species that were detected in 2 or more plots (McCune
and Grace 2002). Species-incidence data were Hellinger-transformed to reduce
the influence of extreme values and increase the linearity of relationships between
species (Legendre and Gallagher 2001). I employed the measured environmental
Figure 2. Correlation between the total incidence of species in quadrats and pitfall traps and
the incidence of detection where the species were collected from a nest within a quadrat
(colony incidence). The points on the graph each represent the relationship between colony
and total incidence for a single species. The correlation value presented in the upper left
is Spearman’s ρ (correlation for rank-transformed data), but the data depicted, to which a
linear model (gray line) is fitted, are not rank transformed. Spearman’s ρ is presented rather
than Pearson’s r because the assumptions of linear regression, homoscedasticity in particular,
are violated. Ten thousand permutations of the data did not yield a single instance with
the absolute value of ρ > 0.86. The deviation of points from the trend line indicate that observations
of some species were more or less commonly made at the nest-site (points falling
above or below the line, respectively). Overall, the total incidence is closely correlated with
the colony incidence, but provides more information, particularly for less common species.
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variables as constraining variables. When the absolute correlation value between
any 2 variables was >0.7, I retained the variable that better explained the variance
in the species data based on constrained eigenvalues from partial RDAs and excluded
the other. Of the variables that remained, I included in the final ordination
only those whose inclusion in the model lowered the AIC value by ≥2 as determined
through forward step-wise selection.
I included data from pitfall traps and all of the quadrat samples in this analysis to
increase the data available for the RDA. However, I combined data from same-transect
quadrats spatially separated by just one meter, but temporally separated within
the same year (first and second 2013 surveys) so that if I observed a species in
both quadrats, I counted it as a single incidence of that species. Three observations
provide support for including the data from both 2013 surveys and both methods in
2012: (1) results of an RDA using the quadrat data alone were qualitatively similar
to the RDA done using all the data; (2) all of the species collected during pitfall
trapping in this study were also collected in quadrat searches, though not always in
the same plot; and (3) evidence of consistent, strong biases for particular species
by either pitfall trapping or quadrat sampling was nearly absent, except perhaps for
Monomorium emarginatum, which was consistently collected more frequently in
pitfall traps. In 2012, I detected 5 species through pitfall traps that I did not detect
in quadrat searches. These species occurred in very few samples overall; thus, their
detection in pitfall traps alone does not necessarily indicate differences in detection
probability based on method, but may simply reflect the benefit of increased
sampling effort of any sort.
Data and code availability. I used the R software and programming language (R
Core Team 2014) for nearly all data manipulations, analyses, and figures. I used the
R libraries car (Fox and Weisberg 2011), reshape2 (Wickham 2007), ggplot2 (Wickham
2009), vegan (Oksanen et al. 2013), and knitr (Xie 2014); and a compilation of
R functions written by K. McGarigal (2014). Rarefaction and calculation of speciesdensity
estimates were the only analyses for which I used additional software. The
data from this study and R code are available online in the Harvard Forest data archives
(http://harvardforest.fas.harvard.edu/data-archive), dataset HF-239.
Results
I collected and identified 16,851 specimens, which were comprised of 53 species
in 21 genera and 4 subfamilies, over 2 years of sampling at the 3 study sites
(Table 3), including 49, 25, and 20 species collected from APBP, SSP, and RSP,
respectively. I detected 28 species at the 2012 plots, and 41 in the 2013 plots. The
41 species I collected from quadrats in 2013 came from just 120 m2 of ground at the
APBP during 16 h of active sampling time. The most frequently collected ant species
in 2012 were Monomorium emarginatum (95 incidences) and Lasius neoniger
(Labor Day Ant) (91 incidences). In 2013, I collected an undescribed species of
Myrmica, designated by André Francoeur as Myrmica sp. AF-smi, most commonly
(52 incidences), followed by Ponera pennsylvanica (Pennsylvania Ponera) and
Aphaenogaster picea (each with 38 incidences).
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The 49 ant species I collected over 2 y of sampling at the APBP and the existing
species list for the preserve (32 species; Barnes 2003), resulted in a combined total
of 53 species recorded at the preserve. This sum represents a 66% increase over the
2003 list. Species present in historical records, but not in my surveys, were Lasius
Table 3 (continued on following page). List of ant species collected from the 3 barrens sites and 2
habitat types.
Subfamily Species APBP RSP SSP Grassland Shrubland
Dolichoderinae Dolichoderus plagiatus (Mayr) x x
Dolichoderinae Dolichoderus pustulatus Mayr x x x x
Dolichoderinae Dolichoderus taschenbergi (Mayr) x x x
Dolichoderinae Forelius pruinosus (Roger) x x
Dolichoderinae Tapinoma sessile (Say) x x x x x
Formicinae Brachymyrmex depilis Emery x x x
Formicinae Camponotus americanus Mayr x x x
Formicinae Camponotus nearcticus Emery x x
Formicinae Camponotus novaeboracensis (Fitch) x x x x
Formicinae Camponotus pennsylvanicus (DeGeer) x x x x
Formicinae Formica argentea Wheeler x x x x
Formicinae Formica dolosa Buren x x x x x
Formicinae Formica exsectoides Forel x x x
Formicinae Formica incerta Buren x x x x x
Formicinae Formica integra Nylander x x x
Formicinae Formica knighti Buren x
Formicinae Formica lasioides Emery x x x x x
Formicinae Formica neogagates Viereck x x x x
Formicinae Formica obscuriventris Mayr x x x
Formicinae Formica pallidefulva Latreille x x x
Formicinae Formica pergandei Emery x x x x
Formicinae Formica rubicunda Emery x x x x
Formicinae Formica subsericea Say x x x x x
Formicinae Lasius alienus (Foerster) x x x x
Formicinae Lasius claviger (Roger) x x
Formicinae Lasius latipes (Walsh) x x
Formicinae Lasius nearcticus Wheeler x x
Formicinae Lasius neoniger Emery x x x x x
Formicinae Nylanderia parvula (Mayr) x x x x x
Formicinae Polyergus lucidus Mayr x x
Formicinae Prenolepis imparis (Say) x x x
Myrmicinae Aphaenogaster picea (Wheeler) x x x x x
Myrmicinae Aphaenogaster rudis Enzmann x x
Myrmicinae Aphaenogaster treatae Forel x x x
Myrmicinae Crematogaster cerasi (Fitch) x x x x
Myrmicinae Crematogaster lineolata (Say) x x x
Myrmicinae Monomorium emarginatum DuBois x x x x
Myrmicinae Myrmecina americana Emery x x x
Myrmicinae Myrmica sp. AF-eva sensu Francoeur x x
Myrmicinae Myrmica sp. AF-smi sensu Francoeur x x x x x
Myrmicinae Myrmica americana Weber x x x x x
Myrmicinae Myrmica detritinodis Wheeler x x
Myrmicinae Myrmica pinetorum Wheeler x x x x
Myrmicinae Myrmica punctiventris Roger x x x x
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interjectus Mayr (Large Yellow Ant), Formica difficilis Emery (Troublesome Ant),
Formica querquetulana Kennedy and Dennis (Oak-grove Ant), and Myrmica fracticornis
Forel (Broken-horned Ant).
During this study effort, I collected 2 species that are particularly uncommon
species for the region: Forelius pruinosus (High Noon Ant) and Formica knighti
(Knight’s Ant). Forelius pruinosus, though common in the southern US, has rarely
been collected in the Northeast, and the record of it at APBP may be the northernmost
record for the Northeast (AntWiki 2015). Formica knighti is a rarely collected
species that had been recorded previously only in Iowa (AntWeb 2015); Minnesota
(AntWiki 2015); Missouri (MacGown 2003); Plymouth County, MA (AntWeb
2015); Martha’s Vineyard, MA (Ellison et al. 2012); and Long Island, NY (S. Cover,
Museum of Comparative Zoology, Cambridge, MA, unpubl. data). Its presence
in SSP in a small clearing dominated by grasses and Comptonia peregrina (L.) J.M.
Coult. (Sweet-fern) is, to date, its northernmost known occurre nce.
Grassland vs. shrubland species density
In all comparisons of grassland and shrubland plots, I detected and estimated
an equal or greater number of species in the shrubland plots than in the grassland
plots (Table 2). I collected 39 species from the shrubland plots and 33 from the
grassland plots when I only considered data from quadrat samples and pitfall traps
(49 and 42 species, respectively, when all specimens were included). The Chao2
point estimates of species density within ten 1-m2 quadrats per plot were higher
for all of the shrubland plots (33 ± 9) than they were for the grassland plots (14 ±
4) (Fig. 3). Welch’s two-sample t-test indicated that there was a significant difference
in species density between the 2 habitat types (t = 3.9, df = 3.7, P = 0.020).
Additionally, the species-accumulation curves from the rarefied quadrat data have
steeper slopes at the level of 10 samples for the shrubland plots than for the grassland
plots, indicating that the sampling effort was less adequate for capturing the
full assemblage in the shrubland than grassland plots and that the Chao2 estimates
should be considered lower-bound estimates for the shrubland plots (Fig. 3).
AR (a grassland plot) was significantly less species-dense than all of the shrubland
plots, and was also significantly less species-dense than the grassland plot
Table 3, continued.
Subfamily Species APBP RSP SSP Grassland Shrubland
Myrmicinae Pheidole pilifera (Roger) x x x x
Myrmicinae Solenopsis molesta (Say) x x x x x
Myrmicinae Stenamma impar Forel x x x
Myrmicinae Temnothorax ambiguus (Emery) x x x x x
Myrmicinae Temnothorax curvispinosus (Mayr) x x x
Myrmicinae Temnothorax longispinosus (Roger) x x
Myrmicinae Temnothorax schaumii (Roger) x x
Myrmicinae Tetramorium caespitum (L.) x x x x x
Ponerinae Ponera pennsylvanica Buckley x x x x
Totals: 49 20 25 42 49
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having the highest incidence of shrub-level oaks (DP). The 2 plots with the highest
estimates of mean species density (KB and BH) were both shrubland plots and had
significantly more species than multiple grassland plots (Fig. 3). Increased canopy
above 0.5 m was associated with higher shrub-level oak density and strongly correlated
with increases in estimated ant-species density (r2 = 0.73, P < 0.0005; Fig. 3).
Grassland vs. shrubland assemblage composition
The separation of shrubland and grassland plots along the first principle axis of
the RDA, which explained 46% of the variance among the transect assemblages,
Figure 3. Species density across study plots. In panels A and B, circles represent grassland
plots and triangles represent shrubland plots. The point estimates for species density and
95% confidence intervals (error bars) based on the Chao2 formula are shown in panels A
and B. In panel A, significant differences as determined by non-overlapping error bars are
indicated by different letters above the error bars. A lowercase letter signifies the plot had
significantly lower species density than plots labeled with the corresponding uppercase
letter. The plot identities are indicated on the x-axis. In panel B, the same data are shown
regressed against the median canopy cover for the plot (transect) on the x-axis. The coefficient
of determination is indicated in the upper left. Panel C shows the species-accumulation
curves (solid lines) and Chao2 estimates (dashed lines) with increasing sample size (1–10
quadrats) based on the rarefied data from the plots. See Table 2 for plot codes.
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indicated that there were compositional differences among ant assemblages occurring
in the different habitats (Fig. 4). The results of the RDA also revealed that 2
constraining variables, 1 related to shrub-level oak coverage and the other to the
proportion of ground covered by dead material, explained 59% of the variation in
the species data based on a permutation test (199 permutations, P = 0.005). Beyond
the clustering of the shrubland plots in the RDA, the APBP sites also separated
slightly from the SSP and RSP sites along the second axis, suggesting possible regional
differences. However, the low proportion of the variance explained by this
axis (13%) and the low replication in SSP and RSP (none in the case of RSP) result
in little statistical power to detect regional or latitudinal differences.
Lasius neoniger and Tetramorium caespitum (Pavement Ant) are common species
of open and highly disturbed habitats in the northeastern US. In the current
study, these species were much more common in the grassland than shrubland plots,
having among the most negative loadings on the first axis of the RDA and highest
goodness-of-fit values (0.87 and 0.86, respectively). In addition to these species,
Figure 4. Redundancy analysis (RDA) of species data consisting of Hellinger-transformed
species incidence from pitfall traps and quadrat searches. The vectors indicate the environmental
variables that contributed to a plausible model (percent dead: the percent of points
along a transect where the ground was at least 50% covered in dead material, and percent
shrub-level oaks: the percentage of sampling points intersected by shrub-level oaks). The
centroid of ant species having goodness-of-fit values >0.70 are indicated by their species code
written in gray (doltas = Dolichoderus taschenbergi, nylpar = Nylanderia parvula, lasneo
= Lasius neoniger, myrpin = Myrmica pinetorum, myrsmi = Myrmica sp. AF-smi, ponpen =
Ponera pennsylvanica, tetcae = Tetramorium caespitum); see the Results section for further
details. Initials inside the symbols indicate the plot identity (see Table 2 for codes).
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Solenopsis molesta (Thief Ant) and Monomorium emarginatum appeared to be
among the dominant species in the grassland habitat (Fig. 5). Many other species
were somewhat shifted toward the shrubland plots in the ordination space. Of these,
Ponera pennsylvanica, Myrmica pinetorum (Ant of the Pines), and Dolichoderus
taschenbergi (Taschenberg’s Dolichoderus) appeared to be the most strongly associated
with the shrubland plots in the ordination space of the RDA and had high
goodness-of-fit scores relative to the other species (goodness of fit values = 0.73,
0.88, and 0.95, respectively; Fig. 4).
Discussion
The results of this study add to our understanding of ant species distributions in
the northeastern US. They reveal high overall species density in inland Pitch Pine
Figure 5. Ant-species incidence data from plots at the APBP, pooled across the 2 habitat
types. The ant species on the x-axis are listed in order of decreasing incidence in the shrubland
habitat. The values on the y-axis are the proportion of each species’ incidence to the
total ant species incidences within the habitat type.
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barrens, with higher species density in shrubland than in grassland habitats. The
higher richness in shrubland habitats was accompanied by some species being more
strongly associated with this habitat type. My sampling methods and analysis detected
convincing patterns in the distributions of abundant species and species with
strong habitat fidelity, but may have lacked the power to detect differences among
rare or less-strongly habitat-affected species. Additional sampling in these habitats
is likely to reveal not only additional species, but also additional patterns in species’
habitat preferences. At the current state of understanding, I recommend conserving
pine barrens and both these habitat types within pine barrens to conserve regionally
and perhaps globally rare ant species.
In the Northeast, similar densities of ant species to those found in the APBP
(41 species from 120 m2 in 16 person-hours) are rarely encountered (Ellison et
al. 2007), and similar ant species richness (53 species at the APBP) in a preserve
or other similarly sized landscape have been reported only after much more extensive
sampling or in other pine barrens. For example, a survey of ants across 9
habitat types within Acadia National Park, ME, conducted by 34 volunteers for a
time period of up to 5 hours (for a total of 170 person-hours) produced 42 species
(Ouellette et al. 2010), and 10 years of exhaustive sampling in an Albany County,
NY, woodland yielded just 14 species from a 2512-m2 search area (Herbers 2011).
Extensive sampling of Vaccinium sp. (blueberry) fields in Maine over a 6-year period
yielded at most 27 species in any single field-type for any given year (Choate
and Drummond 2012). In comparison, 5 of the 12 APBP plots yielded >30 species
when I included data from all collection methods; 44 was the maximum number of
species observed at any single plot (this was observed at BH). Finally, the species
richness that I detected at APBP (53 species) is greater than those recorded from 50
of 67 counties in New England (Ellison and Gotelli 2009).
Other well-surveyed pine-barrens systems in the Northeast also show high
ant-species richness. Seven years of intensive collecting across Nantucket Island
resulted in the collection of 58 species, 54 of which occurred in sandy barrens habitats
(Ellison 2012a). Forty-two ant species have been recorded from the Montague
Sandplains in Massachusetts, another inland pine-barrens system at approximately
100 masl and similar latitude (41.56°N; Ellison and Gotelli 2009). Considered together,
these findings suggest that the high level of species richness at the APBP is
not idiosyncratic, but rather is characteristic of pine-barrens habitats.
I found evidence of higher ant-species density associated with shrublands than
grasslands in northeastern US inland Pitch Pine barrens. Not only were my estimates
of species density for each of the shrubland plots higher than any for the grassland
plots, but the combined shrubland plots yielded more species than the combined
grassland plots even though there were twice as many grassland plots as shrubland
plots and the grassland plots were distributed over a broader longitudinal (but still
narrow latitudinal) range. Due to uneven sampling of habitat types across years,
any inter-annual fluctuations in ant abundance could have contributed to the observed
assemblage differences between habitats. However, in the absence of severe
disturbances or invasive species, there is scant evidence suggesting that species
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2015 Vol. 22, No. 3
densities or ant-assemblage profiles vary substantially across years at this latitude
(Herbers 2011), and the influential role of habitat type for ant assemblages is well
documented. My results of higher ant-species density in shrublands are supported
by similar findings from other regions and ecosystems (Bestelmeyer 2005, Ellison
2012a, Kumschick et al. 2009, Wiezik et al. 2013), and the differences I observed
in assemblage profiles agree with known species’ habitat preferences. Further evidence
supporting the predominant influence of habitat in my study can be seen in
the case of the 2 grassland plots sampled in 2013 along with the 4 shrubland plots.
These 2 grassland plots were not grouped with the shrubland plots in the RDA,
and had lower predicted species densities than the shrubland sites, even though
they were sampled during the same time periods and with identical methods. Variables
that might explain the higher species richness in shrubland plots include the
availability of food resources, particularly the abundance of honey-dew-secreting
hemipterans (Choate and Drummond 2012, Wheeler 1991); higher habitat heterogeneity
on the ground (Graham et al. 2009), allowing both forest and field species
to find suitable nesting sites (Bestelmeyer 2005, Dangerfield et al. 2003, Wiezik et
al. 2013); and the type and frequency of disturbance (Philpott et al. 2010) (e.g., all
of the shrubland plots had been burned within the previous 10 years, whereas the
grassland plots, other than DP, had not). Identifying the factors that directly affect
the ant assemblages in these habitats could be useful in tracking ecosystem recovery
and guiding management decisions.
The extent to which it is possible to draw broad inferences about the relative
species richness of ants in grasslands and shrublands based on my results is limited
by the small sample size, geographic range, and number of habitats considered. It is
possible, for instance, that habitats in the northeastern US with much higher shrub
densities may have fewer ant species than the shrublands and grasslands surveyed
in this study. Other research suggests that ant species density tends to decrease in
forests at similar latitudes, presumably due to the cooler temperatures under shade
(Gotelli and Ellison 2002). Therefore, shade-producing canopy would be expected
to increase with increasing shrub density and could eventually lead to some ant species
being thermally excluded. Nevertheless, my findings show that at the APBP,
where shrubland habitat is intentionally created, differences between grassland
and shrubland invertebrate assemblages are measurable, potentially ecologically
significant, and deserving of further investigation.
There is great potential to gain insight into factors mediating ant-assemblage
structure in the northeastern US through studying their dynamics in pine-barrens
systems. These systems experience a wide range of daily and annual temperature
variation, contain a variety of plant communities, and occur on fairly consistent
substrates. Ants have been used as indicator species in other parts of the world, but
most of these cases are restricted to warm regions with high diversity in species
and functional groups (Ellison 2012b). In the Northeast, ant assemblages may peak
with respect to species richness in pine barrens, and should therefore be investigated
for their potential as indicators in these habitats. As pine barrens continue to
be restored through management, monitoring changes in the ant assemblages could
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provide useful insights into how this important taxon might be expected to respond
to the rising temperatures expected with climate change in the context of a northern
temperate biome and its current suite of biota.
The high ant-species richness that currently exists in pine barrens may benefit
the region as a whole as the regional climate changes. One concern regarding climate
change is that many species will not be able to disperse rapidly enough to
keep pace with the northward-shifting climate (Schloss et al. 2012). Because pine
barrens currently serve as northern range extensions for some southern and more
heat-tolerant species, these habitats could help to maintain overall ant-species
density by providing northern occurrences of heat-tolerant species to replace
heat-intolerant species that may be lost. The incidence of Forelius pruinosus and
Formica knighti—both of which are rarely collected in the Northeast—points to
the value of pine barrens with regard to regional ant biodiversity.
Acknowledgments
This work would not have been possible without the tremendous help and support of
Aaron Ellison (Harvard Forest, Petersham, MA) and Paul Sievert (Department of Environmental
Conservation, University of Massachusetts, Amherst, MA). I thank The Nature
Conservancy (TNC), The New York State Department of Environmental Conservation
(DEC), and the APBPC for granting me permission to collect specimens in these New
York State pine barrens. Staff at the APBP, including Chris Hawver, Neil Gifford, Joel
Hecht, Amanda Dillon, Jesse Hoffman, Erin Kinal, and others, including Chris Zimmerman
(TNC), Kathleen O’Brien (DEC), and Bernard Davies provided a great deal of feedback
and knowledge of the barrens systems investigated in this study. Claudia Knab-Vispo and
Conrad Vispo (Hawthorne Valley Farm and the Farmscape Ecology Program, Ghent, NY),
Jesse Hoffman, and Amanda Dillon provided valuable help with plant identifications, and
Amanda Dillon contributed many hours of assistance sorting and identifying ant specimens.
Brian Hall at Harvard Forest generously created the first figure in this manuscript.
I also thank Matt Lau at Harvard Forest for much helpful feedback on earlier versions of
this manuscript, as well as 2 anonymous reviewers for their constructive suggestions that
greatly improved the final version. This research was supported by the Department of Environmental
Conservation at the University of Massachusetts Amherst, and grant number
DE-FG02-08ER64510 from the US Department of Energy, awarded to Aaron Ellison.
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