Soil Macroinvertebrate Diversity in Congaree National Park

Joseph M. Quattro, Sharon A. Kendrick

Please cite this publication as:

Quattro, J.M., and S.A. Kendrick. 2026. Soil Macroinvertebrate Diversity in Congaree National Park. Science Report NPS/SR—2026/453. National Park Service, Fort Collins, Colorado. https://doi.org/10.36967/2318306

Abstract

Soil macroinvertebrates are important regulators of decomposition, nutrient cycling, and soil structure, yet baseline inventories remain limited for many protected areas. We conducted a DNA-based survey of soil macroinvertebrate diversity in Congaree National Park, South Carolina, across bottomland, bluff, and upland habitats. From September 2022 to October 2024, we collected 74 soil, litter, and opportunistic samples using standardized pit excavation and searches of the litter, with the goal of documenting macroinvertebrate occurrence. Individual specimens were identified using standard mitochondrial cytochrome c oxidase I (COI) DNA barcoding, and Shannon diversity indices were used to compare diversity among substrates and habitat types.

A total of 766 macroinvertebrates were recovered, of which 537 (70%) were confidently assigned to family, genus, or species. Litter samples yielded more individuals than soil samples (546 vs. 220) and exhibited significantly higher Shannon diversity (mean H = 1.426 ± 0.700 vs. 0.961 ± 0.637; P = 0.008). Diversity also differed among ecotones, with bottomland habitats showing the highest mean diversity (H = 1.682 ± 0.715), significantly exceeding both bluff and upland habitats. Eight firefly larvae were recovered and assigned to the genus Photuris; all were found in litter or opportunistic collections rather than excavated soil. Multiple introduced taxa were also detected, some widespread, including Amynthas corticis, A. gracilis, A. minimus, Brachyponera chinensis, Solenopsis invicta, and Oxidus gracilis.

These findings provide the first DNA-based baseline inventory of soil macroinvertebrates for Congaree National Park and show that litter and bottomland habitats support comparatively high diversity. The study also identifies management-relevant taxa, including introduced invertebrates and Photuris larvae, and establishes a foundation for future monitoring of belowground biodiversity across the park.

Image shows two photos side by side of macroinvertebrates sampled during the course of the study. Left: American giant millipede” (Narceus cf. annularis); right: firefly larvae (Photuris sp.).
Representative macroinvertebrates sampled during the course of the study: left, firefly larvae (Photuris sp.) and right, American giant millipede (Narceus cf. annularis).

NPS / SHARON KENDRICK

Acknowledgments

For their help with both field sampling and in the laboratory, the authors gratefully acknowledge the assistance of Chloe Holmes, Ben Kistler, Taylor Smith, and Alexis Yum. This project was made possible through support from the National Park Service Inventory and Monitoring Division.

Introduction

Soil macroinvertebrates, including earthworms, ants, millipedes, centipedes, and beetle larvae, are fundamental components of terrestrial ecosystems. Defined as invertebrates exceeding two millimeters in length, they significantly influence soil structure, fertility, and biodiversity (Decaëns et al. 2006; Lavelle et al. 1997). Occupying diverse functional niches—detritivores, herbivores, predators, and ecosystem engineers—macroinvertebrates form key linkages between above- and below-ground processes, thereby regulating ecosystem functioning across spatial and temporal scales (Bardgett and van der Putten 2014; Jouquet et al. 2006).

A central feature of many macroinvertebrates is their role as ecosystem engineers (Jones et al. 1994; Wright and Jones 2006). Burrowing activity, for example, modifies soil porosity and hydrological properties, improving aeration and infiltration while reducing runoff and erosion (Blouin et al. 2013; Bottinelli et al. 2020). These bioturbation processes not only maintain soil structure but also enhance ecosystem resilience to disturbances such as drought, flooding, and habitat alteration.

Macroinvertebrates also drive organic matter decomposition and nutrient cycling. Detritivores fragment litter and ingest organic residues, increasing surface area for microbial colonization and accelerating decomposition (Brown et al. 2000; Frouz 2018). Their digestive processes convert organic matter into more labile compounds that stimulate microbial activity and enzyme production, enhancing mineralization of nitrogen, phosphorus, and other nutrients. These processes directly promote soil fertility and primary productivity (Fonte et al. 2012; Lubbers et al. 2020; van Groenigen et al. 2014).

Beyond these functional roles, macroinvertebrates are widely recognized as indicators of soil quality and ecosystem health. Their abundance and diversity respond sensitively to land-use change, pollution, and management practices, making them reliable proxies for soil condition (Decaëns et al. 2006; Velasquez et al. 2012). Standardized sampling protocols have facilitated comparative analyses across ecosystems, revealing strong biogeographic variability in macrofaunal communities and highlighting both their ecological importance and vulnerability to anthropogenic pressures (Lavelle et al. 2022).

Finally, macroinvertebrates mediate above- and below-ground interactions that influence entire ecological networks. By altering nutrient availability, root herbivory, and rhizosphere microbial dynamics, they indirectly affect plant performance, community composition, and higher trophic interactions (Bardgett and Wardle 2010; Wardle et al. 2004). These cascading effects underscore the broad ecological significance of soil macroinvertebrates, positioning them as pivotal components of biodiversity conservation and sustainable land management.

Biodiversity conservation is a central concern of conservation biology, a discipline that emerged to address the accelerating rates of species extinction and habitat loss (Soulé 1985). Biodiversity encompasses variation at genetic, species, and ecosystem levels, and this variation is critical for maintaining ecological processes, resilience, and evolutionary potential (Cardinale et al. 2012; Díaz et al. 2019). The rapid loss of biodiversity, driven by habitat destruction, overexploitation, pollution, climate change, and invasive species, has accelerated species extinction rates to levels estimated to be 100–1,000 times higher than the natural background rate (Pimm et al. 2014).

The consequences of biodiversity loss extend beyond ecological integrity, as ecosystems provide essential services such as pollination, nutrient cycling, climate regulation, and disease control (Millennium Ecosystem Assessment 2005). For human societies, these services underpin food security, health, and adaptation to environmental change (IPBES 2019). In addition, biodiversity represents an irreplaceable component of evolutionary history and cultural value, making its protection both an ecological and ethical imperative (Wilson 2016).

Conservation biology therefore seeks to understand the drivers of biodiversity decline and to develop strategies that safeguard species and ecosystems. This includes not only the preservation of endangered species but also the management of landscapes and restoration of altered ecosystems (Meine et al. 2006). Addressing biodiversity loss requires quite dramatic changes that involve ecological, social, and economic systems (Díaz et al. 2019).

Against this backdrop, Congaree National Park (CONG) provides an ideal setting to establish a molecular and ecological baseline for soil biodiversity across distinct ecotones. Our objectives are threefold: (i) to develop a baseline inventory of macroinvertebrate diversity that can support future assessments of habitat alteration and climate change; (ii) to compare within‑ and among‑ecotone biodiversity using diversity indices; and (iii) to document taxa of particular management interest—specifically firefly (genus Photuris) larvae and introduced species—along with their habitat associations and distributions within the park. To our knowledge, this is the first study at Congaree to apply DNA barcoding of the mitochondrial cytochrome-oxidase I (COI) locus to identify soil macroinvertebrates sampled across 39 sites spanning bottomland, bluff, and upland habitats.

Goals and Objectives

  • Collect soil macroinvertebrate (operationally defined as invertebrates ≥ 2 mm in length) fauna from 39 sites representing three ecotones (bottomland, upland, bluff) in Congaree National Park. Macroinvertebrates will be sampled by excavating pits of 30 cm × 30 cm × 30 cm (27 L) and manual inspection of soil for 60 person-minutes.

  • Use DNA barcoding approaches to identify and characterize individual taxa to the highest taxonomic category.

  • Estimate macroinvertebrate diversity (Shannon diversity index (H)) by habitat type (soil versus litter) and geolocation and statistically analyze species diversity among three unique ecotones: bottomland, bluff and upland regions.

  • Document the existence of introduced macroinvertebrate taxa in the park with a particular focus on the distribution and abundance of annelids across sample park habitats.

  • Identify, locate, and describe the distribution and habitat preferences of firefly larvae at Congaree National Park.

Methods

Site Selection and Soil Macroinvertebrate Sampling

The present survey was designed to prioritize sampling overall species-level biodiversity and occurrence (e.g., presence/absence context) over questions of relative abundance. We sampled 39 sites in Congaree National Park from September 2022 until October 2024 (Figure 1; Table 1). Sites 1–31 comprised soil macroinvertebrates sampled from an excavated pit (“S” samples) and a contemporaneously sampled litter layer (“L” samples). Sites 32–39 were opportunistically sampled sites. Sampling sites were chosen by the park to be representative of distinct habitats (e.g., pine, hardwood, prescribed fire, floods, feral hog rooting) and focus on areas of specific management interest (e.g., impacted canoe launch, boardwalk, areas with known firefly abundance). In addition, we opportunistically sampled sites (e.g., rotting logs, sites near bodies of water, sites near large aggregations of adult fireflies) when the occasion arose. Most sites were sampled once, but two sites were sampled on two occasions to gauge reproducibility. Because no appreciable differences between replicate samples were observed, these two replicates were subsequently pooled for further analysis.

Figure 1. A map of Congaree National Park with sampling locations marked as dots and labeled by site number. Park boundaries are shaded in green. A central cluster of sites is highlighted with a red box, with two inset maps showing zoomed-in views of selected areas.
Figure 1. Sampling locations in Congaree National Park.

NPS / SHARON KENDRICK

Table 1. Sampling sites, date sampled and GPS coordinates of site locations in Congaree National Park. GPS coordinates were recorded in decimal degrees (WGS84). Sites were classified as Bottomland, Upland and Bluff habitats.
Site Classification Latitude (DD) Longitude (DD) Date Sampled Description
1 Upland 33.83089 −80.82517 2022-09-16 Upland forest, unburned, not actively managed for decades
2a Bottomland 33.81843 −80.7874 2023-07-21 Creek bank near South Cedar Creek canoe launch, fishing site
2b Bottomland 33.81843 −80.7874 2023-10-06 Creek bank near South Cedar Creek canoe launch, fishing site
3 Bottomland 33.80981 −80.86636 2023-08-09 Levee and/or oak flat
4 Bottomland 33.80934 −80.86601 2023-08-09 Levee and/or oak flat
5 Upland 33.8329 −80.80401 2023-05-23 High-quality longleaf pine habitat
6 Bluff 33.83901 −80.86057 2023-07-12 Bannister Bridge, region of high firefly activity
7 Bottomland 33.83057 −80.82599 2022-09-23 Muck Swamp, wet, organic-rich soils adjacent to the bluff
8 Bluff 33.8334 −80.82875 2023-05-04 Bluff between Muck Swamp and firebreak surrounding the Harry Hampton West burn unit. Good firefly activity.
9 Bottomland 33.83284 −80.82954 2023-05-04 Muck Swamp, wet, organic-rich soils adjacent to the bluff
10 Upland 33.832 −80.8167 2023-05-10 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
11 Upland 33.83743 −80.8278 2023-05-04 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
12 Upland 33.81362 −80.7713 2023-05-02 Red Bluff Road, recently burned, formerly loblolly pine plantation
13 Upland 33.81234 −80.75162 2023-07-28 Red Bluff Road, recently burned, formerly loblolly pine plantation
14 Upland 33.83259 −80.80569 2023-07-28 High-quality longleaf pine habitat
15 Bluff 33.83018 −80.82464 2022-09-16 Sims Trail, bluff soils toeing out into sandy soil in the floodplain, prime firefly habitat
16 Bluff 33.82983 −80.8186 2022-10-28 Sims Trail, bluff soils toeing out into sandy soil in the floodplain, prime firefly habitat
17 Bluff 33.83161 −80.82633 2022-09-23 Bluff between Muck Swamp and firebreak surrounding the Harry Hampton West burn unit. Good firefly activity.
18 Bluff 33.82968 −80.82463 2022-09-16 Red Bluff Road, recently burned, formerly loblolly pine plantation
19 Upland 33.83202 −80.81864 2023-05-10 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
20 Upland 33.81511 −80.7713 2023-07-28 Red Bluff Road, recently burned, formerly loblolly pine plantation
21 Bluff 33.83027 −80.81778 2022-10-28 Sims Trail, bluff soils toeing out into sandy soil in the floodplain, prime firefly habitat
22 Bottomland 33.82963 −80.81737 2022-11-04 Sandy soils of Dry Branch alluvial fan, near the boardwalk
23 Upland 33.82985 −80.8196 2022-10-28 N/A
24 Bottomland 33.82923 −80.81874 2022-11-04 Sandy soils of Dry Branch alluvial fan, near the boardwalk
25 Bluff 33.82954 −80.82054 2022-10-07 Upland forest, unburned, not actively managed for decades
26a Bottomland 33.81835 −80.7886 2023-04-20 Creek bank near South Cedar Creek canoe launch, fishing site
26b Bottomland 33.81835 −80.7886 2023-10-06 Creek bank near South Cedar Creek canoe launch, fishing site
27 Bottomland 33.819 −80.78812 2023-07-12 Creek bank near South Cedar Creek canoe launch, fishing site
28 Upland 33.83492 −80.82626 2023-07-12 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
29 Upland 33.8358 −80.8264 2023-06-16 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
30 Upland 33.8356 −80.827 2023-06-16 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
31 Bluff 33.82987 −80.82499 2023-07-21 Sims Trail, bluff soils toeing out into sandy soil in the floodplain, prime firefly habitat
32 Upland 33.83572 −80.82588 2023-09-21 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
33 Upland 33.8364 −80.82696 2023-09-21 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
34 Upland 33.8358 −80.8275 2023-09-21 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns, leaf litter sample
35 Upland 33.8358 −80.8275 2023-09-21 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns, rotting stump sample
36 Rotting Log 33.8347 −80.8259 2023-09-12 Opportunistic site, rotting log
37 Upland 33.835 −80.8259 2023-09-12 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
38 Upland 33.8356 −80.8259 2023-09-12 Harry Hampton West, second growth mixed-pine-and-hardwood subjected to mastication and prescribed burns
39 Bottomland 33.8189 −80.7872 2023-10-06 Creek bank near South Cedar Creek canoe launch, fishing site

At each location, GPS was used to locate and uniquely tag each sampling location (i.e., the focal point), and a ground temperature was recorded at this focal point (e.g., at the surface of the litter layer = temp1). A photograph was taken of the general area to document the surrounding habitat. The surrounding litter was carefully removed from the focus of the site outwards to approximately two meters and any macroinvertebrates (operationally defined as invertebrates ≥ 2 mm in length) encountered in the litter were preserved immediately (and separately from soil macroinvertebrates) in absolute ethanol. Soil surface temperatures (litter removed = temp2) were taken using a General IRT3 infrared thermometer prior to soil excavation and then pits of 30 cm × 30 cm × 30 cm (27 L) were excavated at each site A PVC square of dimensions 30 cm × 30 cm was used to standardize pit size. Soil temperature at the bottom of each pit (= temp3) was recorded and then two walls of each pit were cleaned prior to being photographed with an included size standard (Figure 2). Excavated soil was removed to a white PVC tarpaulin where small homogeneous soil samples were removed and placed in individual Whirl-Pak bags. Excavated soil was examined for a period of 60 person-minutes involving a minimum of two and upwards of four individuals. We found that this 60-minute search period was sufficient for a thorough examination of all excavated soil, longer search periods (e.g., 90 or 120 person-minutes) failed to uncover additional sampled macroinvertebrates (Quattro, pers. obs.). All macroinvertebrates from the soil layers were immediately preserved separately from the litter fauna in 100% ethanol for transfer to the laboratory. At the conclusion of a 60 person-minute search, the soil and then litter layer were replaced at each site. Preserved samples were transported to the University of South Carolina and stored at room temperature until further manipulation. After several days, or when the ethanol became noticeably cloudy, the ethanol was carefully decanted from each sample and replaced with fresh preservative. Soil samples for moisture analysis were stored at 4 ℃ until further analysis (see below).

Figure 2. Three side-by-side photos showing an example collecting site in a forest. The left image shows a leaf-littered forest floor with trees and understory vegetation. The center image shows a soil pit with exposed roots, a measuring instrument, and a labeled sample bag. The right image shows a cleared patch of ground with soil and leaf litter spread on a white sheet.
Figure 2. Example of a sampling site (Site 1 in Table 1). Leaf litter was removed and a soil pit was excavated, with excavated soil spread on a white surface for examination.

NPS / SHARON KENDRICK

DNA Barcoding

Individual macroinvertebrates from each sampling site were moved from bulk storage to individual containers and assigned a unique code. Prior to DNA isolation, all macroinvertebrates were photographed using a Leica EZ4D dissecting stereo microscope fitted with a camera. DNA was extracted from each specimen using Qiagen DNeasy blood and tissue DNA extraction kits following the manufacturer’s protocol (Qiagen Corporation, Maryland, USA). The presence and quality of template DNA was confirmed by 1.5% agarose gel electrophoresis with ethidium bromide staining. The resulting DNA was stored at −80 ℃ until further analysis.

The taxonomic identity of all soil macrofauna was determined to the highest taxonomic category possible by PCR amplification and sequencing of the universal COI fragment (mitochondrial Cytochrome Oxidase I) using methods and primer sets described in Folmer et al. (1994). After amplification success was determined by agarose gel electrophoresis, the PCR products were sequenced on an ABI 3130 automated sequencer using BigDye terminator sequencing (v 3.1, ThermoFisher Scientific, Waltham, Massachusetts, USA). DNA sequences obtained were visually inspected and edited using Sequencher (version 4.1; Genecodes Corporation, Michigan, USA) then parsed for analyses for taxonomic identification.

Standard DNA barcoding approaches (e.g., Hebert et al. 2003) were used to identify individual invertebrate samples to the highest taxonomic category whenever possible. Briefly, identifications used traditional “BLAST” (Altschul et al. 1990) based searches using individual unknown COI sequences as queries against those accessed in the GenBank DNA sequence repository. “Percent Identity” (PI) and “Query Coverage” (QC) were evaluated to determine the best taxonomic match for each individual query sequence. Taxonomic cutoffs were determined by use of thresholds commonly applied in COI barcoding studies (e.g., Hebert et al. 2003, Virgilio et al. 2010). Accordingly, we used the following PI metrics:

  • PI ≥ 98%: confident species level identification

  • PI ≥ 95 – ≤ 97%: confident genus level identification

  • PI ≥ 90 – ≤ 94%: confident family level identification

These conservative PI cutoff thresholds align with observed COI intraspecific relative to interspecific PI scores across various insect orders (e.g., Virgilio et al. 2010). As an example—when an individual barcode matched individuals accessed in GenBank with a ≥98% PI and sequences exhibited high QC (≥95%), they were assigned as species. When barcodes returned PI scores of <90% or those with low QC they were reported at higher taxonomic levels if confidence warranted or left as unassigned. This was done to ensure conservative taxonomic calls that align with common COI barcoding practices in invertebrate biodiversity studies.

Soil Moisture and Organic Content

We determined soil moisture and organic content at each location via standard gravimetric methods (e.g., Black 1965). Briefly, small soil samples (~3 grams) were removed from (4 ℃) storage, placed in individual aluminum pans (three replicate samples/site) and weighed to the nearest hundredth of a gram. These samples were then heated to 105 ℃ for 24 hours and then weighed. Heating was repeated for an additional several hours until no appreciable decrease in weight was observed. Soil organic content was estimated by heating individual soil samples used for the moisture content analyses to 360 ℃ for two hours after which each sample was weighed. Weights before and after heating were used to determine moisture and organic content, respectively (standardized as a percentage of total wet weight). All analyses were done in triplicate and presented as the arithmetic mean of percent weight change (± 1 standard error).

Diversity Indices

We calculated Shannon Diversity Indices (H) for each site (Shannon 1948), excluding any samples that could not be confidently assigned to a taxonomic category (e.g., all N/A in Appendix). A one-way ANOVA was used to test for significant differences in Shannon Diversity between the three habitat types sampled (Bottomland, Bluff, Upland). Fisher’s LSD (Fisher 1935) was used to test, pairwise, mean Shannon diversity by habitat type. A t-test was used to compare mean Shannon diversity between pooled soil and litter samples. All statistical comparisons were performed with StatPlus: mac (build 8.0.4.0, AnalystSoft, Inc.). Soil and litter samples from each site were treated as separate samples in all statistical comparisons.

Results

Environmental Observations

We surveyed a total of 33 soil samples (30 × 30 × 30 cm pits or 27 L of soil per sampling location), 33 litter samples (~12.6 m2 per sample) and eight opportunistic sites (litter and/or decaying vegetation) for a total of 74 individual samples. From these 74 samples, we recovered a total of 766 individual macroinvertebrates (Table 2). The number of individual macroinvertebrates sampled per sampling location averaged 11.1 individuals (± 13.8 (SD)). Macroinvertebrates were more commonly encountered in the 41 litter samples (n = 546, x̅ = 13.3 inds/site) than in 33 soil samples (n = 220, x̅ = 6.7 inds/site). The distribution of individuals per site, by matrix (soil versus litter), was uneven, ranging from zero at three soil sites (5, 22, 28) to a maximum of 86 individuals in a single litter sample (site 4).

Table 2. Number of Individual Macroinvertebrates (length > 0.5 mm) Collected per Site. Abbreviations: S = soil sample, L = litter sample.
Site Number of Specimens
1L 5
1S 3
2aL 33
2bL 10
2aS 10
2bS 0
3L 38
3S 29
4L 86
4S 9
5L 5
5S 0
6L 51
6S 23
7L 11
7S 16
8L 6
8S 1
9L 10
9S 3
10L 14
10S 3
11L 5
11S 2
12L 8
12S 3
13L 6
13S 6
14L 2
14S 4
15L 10
15S 8
16L 2
16S 1
17L 5
17S 7
18L 10
18S 10
19L 6
19S 5
20L 9
20S 9
21L 7
21S 7
22L 6
22S 0
23L 4
23S 9
24L 11
24S 4
25L 14
25S 19
26aL 27
26bL 16
26aS 3
26bS 0
27L 24
27S 11
28L 7
28S 0
29L 3
29S 4
30L 17
30S 6
31L 14
31S 5
32L 15
33L 5
34L 2
35L 13
36L 9
37L 3
38L 15
39L 1
Sum 766

Soil temperatures across all sampling locations (= temp3) ranged from 15.9 to 21.6 ℃ (x̅ = 20.1 ℃) whereas the litter temperature (= temp1) ranged from 15.3 to 35.1 ℃ (x̅ = 23.6 ℃) (Table 3). Soil temperature and litter temperature were strongly positively correlated (r = 0.79, p < 0.001). Moisture and organic content varied considerably by site, ranging from 4.3% to 50.4% (x̅ = 20.4%) and 1.8% to 33.6% (x̅ = 8.2%), respectively. Moisture content and organic content by sampling location were positively correlated (r = 0.56, p = 0.002). There was no relationship between the number of individual macroinvertebrates sampled at each site (by sample type: litter versus soil) and temperature, soil moisture content, or organic content (all correlation p-values >0.05). However, the absolute number of macroinvertebrates sampled in soil was positively correlated to the count of individuals sampled in the litter (r = 0.45, p = 0.01), suggesting that macroinvertebrate abundance, soil and litter, might be driven by the local environment.

Table 3. Soil temperature, moisture content and organic content determined across sampling sites in Congaree National Park. Soil samples were not taken at sites 32–39, only litter samples were surveyed. The thermometer malfunctioned at sites 10 and 19, therefore, no temperature readings were taken. Temperatures are shown as temp1/temp2/temp3, see Methods for description.
Site Soil Temperature (°C) Soil Moisture (% ± SE) Organic Content (% ± SE)
1 22.4 / 21.0 / 19.6 13.67 ± 0.00 3.01 ± 0.00
2a 25.7 / 24.6 / 21.7 15.02 ± 0.74 4.37 ± 0.41
2b 21.8 / 21.4 / 20.2 14.78 ± 0.75 4.63 ± 0.41
3 27.0 / 25.9 / 22.4 25.00 ± 0.69 11.10 ± 0.47
4 24.6 / 24.3 / 23.7 28.42 ± 0.28 13.55 ± 0.75
5 27.0 / 25.7 / 20.5 29.60 ± 4.03 33.56 ± 3.33
6 26.3 / 23.2 / 21.1 26.83 ± 4.45 18.75 ± 8.68
7 20.1 / 18.6 / 15.9 9.39 ± 0.00 5.70 ± 0.00
8 22.3 / 20.2 / 19.8 22.99 ± 2.52 5.30 ± 2.38
9 21.7 / 17.8 / 16.3 24.46 ± 0.95 11.84 ± 0.21
10 – 11.55 ± 0.30 1.82 ± 0.03
11 25.1 / 19.3 / 18.9 11.71 ± 0.44 9.43 ± 1.03
12 33.2 / 29.6 / 26.1 22.28 ± 0.54 12.97 ± 0.75
13 30.1 / 26.7 / 24.3 23.81 ± 0.28 15.87 ± 0.41
14 22.2 / 21.1 / 19.6 15.01 ± 0.85 3.09 ± 0.28
15 23.0 / 21.9 / 21.1 14.49 ± 0.00 6.80 ± 0.00
16 18.5 / 16.8 / 16.3 50.43 ± 0.69 16.50 ± 0.44
17 18.6 / 18.2 / 20.2 9.80 ± 0.00 3.47 ± 0.00
18 24.7 / 22.8 / 21.5 25.80 ± 0.00 6.12 ± 0.00
19 – 8.29 ± 0.23 1.79 ± 0.10
20 35.1 / 30.3 / 24.7 16.80 ± 1.10 14.23 ± 0.66
21 18.6 / 16.7 / 16.1 33.73 ± 0.00 7.42 ± 0.00
22 20.3 / 17.7 / 16.3 34.48 ± 0.01 5.87 ± 0.01
23 15.3 / 16.3 / 17.7 4.53 ± 0.01 2.36 ± 0.01
24 16.5 / 17.1 / 17.7 22.23 ± 0.00 3.75 ± 0.00
25 16.9 / 18.6 / 19.2 21.48 ± 0.00 4.07 ± 0.00
26a 21.0 / 20.8 / 17.3 24.87 ± 2.71 11.33 ± 0.47
26b 19.8 / 18.9 / 18.2 30.09 ± 4.68 11.87 ± 1.66
27 27.2 / 22.1 / 20.1 16.22 ± 0.26 4.61 ± 0.18
28 25.5 / 23.0 / 21.1 13.00 ± 0.33 2.16 ± 0.13
29 24.6 / 21.6 / 20.5 19.22 ± 0.34 5.17 ± 0.03
30 26.0 / 23.4 / 20.8 12.56 ± 0.59 2.27 ± 0.10
31 24.4 / 23.6 / 21.6 20.92 ± 1.10 4.03 ± 0.36

DNA Barcoding

We were able to confidently assign 537 of the 766 (70%) individual macroinvertebrates sampled to a taxonomic rank of family, genus or species (Tables 4 and 5). Of these three taxonomic ranks, we could confidently (and uniquely) assign 2.4% of the 537 individuals no higher than family (PI ≥ 90 – ≤ 94%, QC > 95%), 29.1% no higher than genus (PI ≥ 95 – ≤ 97%, QC > 95%), and 68.5% to species (PI ≥ 98%, QC > 95%). While we consider a 70% success rate for taxonomic assignment quite good for such a broad survey, the taxonomic coverage of the current GenBank repository is likely limited in invertebrate diversity and therefore reduces our ability to fully and confidently assign all individuals. Certainly, as taxonomic coverage in GenBank increases as accessions from more taxon-focused barcoding studies are completed, our sequences might then be used for higher level assignments, iteratively over time.

Table 4. Introduced species encountered across sampling sites in Congaree National Park. Abbreviations: #S = soil sample, #L = litter sample. Taxonomic names are sorted alphabetically and not by taxonomic group.
Taxon (n) Site(s)
Amara aenea 19S
Amynthas corticis 1S, 2aL, 3L, 3S, 4L, 6L, 6S, 7S, 18L, 21S, 22L, 25S, 27L, 31L, 31S
Amynthas gracilis 3L, 3S, 4L, 6S, 24S, 25S, 27S
Amynthas hupeiensis 3S
Amynthas minimus 3S, 4S, 6S, 27S
Brachyponera chinensis 2bL, 24L, 26aL, 26bL, 27L, 27S, 32L, 38L
Dendrobaena attemsi 21S
Dendrobaena hortensis 14S
Harmonia axyridis 26aL
Hypoponera opaciceps 3L
Metaphire californica 2aS, 4L, 6S
Octolasion cyaneum 3S
Oxidus gracilis 6L, 6S, 26bL, 27L, 27S
Popillia japonica 4S
Solenopsis invicta 5L, 6L, 10L, 11L, 12L, 14L, 20L
Strigamia acuminata 35L
Velarifictorus micado 4L

Table 5. Total number of soil macroinvertebrates sampled summed across sites by substrate type (Leaf Litter: LL, S: Soil) and the number of individuals confidently assigned to Family or above. Percentages are calculated relative to the total number of individuals sampled across substrate and site (766).
Category Leaf Litter Soil Sum
Sampled by Substrate 546 (71%) 220 (29%) 766
Identified > Family Level 387 (50%) 150 (20%) 537 (70%)

Shannon Diversity and Habitat Type Comparisons

Shannon diversity (H) indices ranged from 0 to 2.938 (Appendix). Mean Shannon diversity (H) was significantly higher in litter samples (mean H = 1.426 ± 0.700 (SD)) than soil samples (mean H = 0.961 ± 0.637 (SD)) (F = 7.506, P = 0.008) (Figure 3).

Figure 3. A plot showing mean Shannon diversity for litter and soil habitat types with 95% confidence intervals. Litter has a higher mean diversity than soil, with minimal overlap between the confidence intervals.
Figure 3. Mean Shannon diversity as a function of sample type (Soil, Litter). 

NPS / JOE QUATTRO

An ANOVA found significant differences in mean Shannon diversity among the three habitat types (with soil and litter samples treated separately within habitat type) (F = 5.852, df = 2, P = 0.005). Bluff (mean H = 1.025 ± 0.698 (SD)) and Upland (mean H = 1.043 ± 0.664 (SD)) habitat samples were significantly less diverse than Bottomland samples (mean H = 1.682 ± 0.715 (SD)) (Figure 4). Fisher’s LSD comparisons found significant differences in Shannon diversity (H) between Bottomland and either Bluff (t = 2.907, P = 0.005) or Upland (t = 3.139, P = 0.003), but not between Bluff and Upland samples (t = 0.088, P = 0.930).

Figure 4. A plot showing mean Shannon diversity for bluff, bottomland, and upland habitat types with 95% confidence intervals. Bottomland has the highest mean diversity, while bluff and upland have lower and similar mean values.
Figure 4. Mean Shannon diversity as a function of habitat type (Upland, Bottomland, Bluff).

NPS / JOE QUATTRO

Firefly Larvae

Within the confident taxonomic assignments that we recovered, several are highlighted here as either promising or problematic for Congaree National Park. We encountered a total of eight firefly larvae in our sampling and could confidently assign these eight individuals to the genus Photuris (Table 5). Database searches returned evidence for three different species within the genus Photuris, although only one could be confidently assigned to species (Photuris quadrifulgens). The other two species were closely related to Photuris tremulans and Photuris lucicrescens, although these assignments should be considered provisional given the taxonomic coverage currently available in the GenBank sequence repository. These latter two groups could not be confidently assigned to species since PI scores were below 98%. Although we found no firefly larvae in the 33 soil samples we excavated in Congaree National Park, we sampled larvae in our litter and opportunistic collections. Photuris cf. tremulans larvae were found in litter samples 2 (n = 4) and 4 (n = 2), a Photuris quadrifulgens larva was sampled from litter at site 4 (n = 1), and a larval Photuris cf. lucicrescens was found in litter at site 32 (n = 1) (Table 5). Two sites (2 and 4) were Bottomland habitats whereas the third was considered Upland (32).

Introduced Species

The introduction of exotic species into ecosystems in the United States has caused widespread economic, biological and ecosystem damage and, in many cases, local extinctions. During our surveys we encountered several introduced species among our DNA barcodes (Table 4). Invasive (putatively from the neotropics) earwigs (Euborellia arcanum) were found in litter from sites 2 and 35. Invasive jumping worms (genus Amynthas) were relatively widespread in our samples. Green jumping worms (Amynthas corticis) were sampled in soil from sites 1, 3, 6, 21, 25, 31 and litter from sites 2, 4, 6, 18, 22, 27, and 31. Thin jumping worms (Amynthas gracilis) were sampled in soil from sites 3, 6, 24, 25 and 27 and in litter at sites 3 and 4. Tiny jumping worms (Amynthas minimus) were found in soil from sites 4, 6, and 27 (Table 5). In some sites, these invasive worms were the predominant species. Red imported fire ants (Solenopsis invicta) were found exclusively in litter collected from sites 5, 6, 10, 11, 12, 14, and 20. Greenhouse millipedes (Oxidus gracilis) were found in soil samples from sites 6 and 27, and in litter from sites 6, 26, and 27.

Discussion

This study represents the first large-scale, DNA-based survey of soil macroinvertebrate biodiversity across ecotonal gradients in Congaree National Park. From 74 samples collected between September 2022 and October 2024, a total of 766 individual macroinvertebrates were recovered, of which 70% were successfully assigned to taxonomic rank using barcoding of the COI locus. The high rate of successful identification underscores the value of molecular tools for biodiversity assessment in complex soil communities. However, incomplete representation of many soil-dwelling taxa in GenBank likely limited full species-level resolution. As barcoding databases continue to expand, particularly for macroinvertebrates, the reference sequences produced here will support improved identification accuracy and long-term ecological monitoring (Hebert et al. 2003; Lavelle et al. 2022; Virgilio et al. 2010).

Macroinvertebrate diversity and abundance varied significantly between substrates. Litter samples contained more individuals and exhibited significantly higher Shannon diversity (mean H = 1.426 ± 0.70) than soil samples (mean H = 0.961 ± 0.64; P = 0.008). This pattern is consistent with global findings that litter layers host greater macrofaunal richness than underlying mineral soils due to their structural complexity, high organic content, and greater habitat heterogeneity (Bardgett and van der Putten 2014; Decaëns et al. 2006; Frouz 2018). The litter layer supports diverse functional guilds—including detritivores, decomposers, and predators—that drive nutrient turnover and link above- and below-ground processes (Brown et al. 2000; Lavelle et al. 1997). The observed positive correlation between soil and litter macroinvertebrate abundance suggests that site-level environmental factors, such as resource quality and moisture, jointly influence both layers.

Among habitat types, Bottomland sites exhibited the highest mean Shannon diversity, significantly exceeding both Bluff and Upland habitats. These results are consistent with previous findings that hydric, floodplain environments support elevated biodiversity due to stable microclimates, frequent detrital inputs, and complex vegetation structure (Fonte et al. 2012; Lubbers et al. 2020). Moisture and organic content were positively correlated, reflecting the well-established relationship between water retention and organic matter accumulation (Bottinelli et al. 2020). The lack of strong correlations between total abundance and abiotic factors such as temperature or moisture suggests that community composition is driven by multidimensional habitat attributes, including substrate texture and organic substrate heterogeneity (Jouquet et al. 2006; Wardle et al. 2004).

Firefly larvae (Photuris spp.) were among the most ecologically significant taxa encountered. Eight larvae were recovered exclusively from litter and opportunistic samples, supporting the hypothesis that Photuris larvae inhabit surface organic layers. This finding is notable given the scarcity of data on larval lampyrids in North America and supports prior observations that many firefly species rely on moist, undisturbed litter habitats for development (Riley et al. 2021b). Because fireflies are declining globally due to habitat loss, light pollution, and pesticide exposure (Riley et al. 2021a), the detection of larval Photuris in Congaree highlights the park’s role as a regional refuge for these species and emphasizes the importance of conserving intact litter microhabitats.

Unfortunately, multiple introduced species were identified, including Amynthas corticis, A. gracilis, A. minimus, Brachyponera chinensis, Solenopsis invicta, and Oxidus gracilis. Many of these taxa have been shown to individually disrupt soil structure, nutrient cycling, and native community composition (Blouin et al. 2013; Zhang et al. 2013). The widespread occurrence and taxonomic diversity of Amynthas “jumping worms” are of particular concern, as these invasive annelids rapidly consume leaf litter, reducing organic horizon thickness and altering soil carbon dynamics (Lubbers et al. 2020; Six et al. 2004). Similarly, S. invicta (red imported fire ant) can significantly reduce native arthropod diversity and alter trophic interactions (Decaëns et al. 2006; Velasquez et al. 2012). The identification of such species within Congaree National Park underscores the breadth of issues that managed ecosystems face, while highlighting the importance of sustained monitoring and early management to prevent the introduction and spread of invasives and curtail potential ecosystem-level impacts.

Environmental data revealed a strong correlation between litter and soil temperatures (r = 0.79, P < 0.001), suggesting that surface conditions influence subsurface thermal regimes. Although no significant correlations were found between macroinvertebrate abundance and environmental variables such as temperature, moisture, or organic matter, stable microclimates likely enhance biodiversity by maintaining favorable conditions for detritivore and microbial activity (Bardgett and Wardle 2010). The sampling design prioritized species detection rather than density estimation, and therefore abundance comparisons among sites should be interpreted with caution and with a host of caveats. The conservative identity thresholds applied in COI barcoding (≥98% for species-level identification) ensured reliability but might have led to an underestimation of actual richness (Virgilio et al. 2010). Future studies incorporating seasonal replication, continuous microclimate measurements, and multi-locus genetic approaches could provide a more comprehensive understanding of macroinvertebrate ecology in these environments.

This work establishes an essential molecular and ecological baseline for soil biodiversity in Congaree National Park. Such baselines are critical for detecting future ecological change driven by hydrological variability, invasive species, and climatic fluctuations (Lavelle et al. 2022). Repeated barcoding surveys, when coupled with measurements of ecosystem function (e.g., litter decomposition, nutrient mineralization, and soil respiration), should enable direct assessment of how biodiversity shifts affect ecosystem processes throughout the park’s unique ecotones (Brown et al. 2000; van Groenigen et al. 2014). Furthermore, the development of a curated reference library of Congaree macroinvertebrates contributes to global efforts to document soil biodiversity (Lavelle et al. 2022).

In summary, Congaree’s bottomland forests support the highest soil macroinvertebrate diversity and host both ecologically important native species and, alarmingly, invasive taxa. The litter layer serves as a biodiversity reservoir essential for ecosystem function, while the presence of invasive earthworms and ants highlights ongoing threats to soil integrity and native macroinvertebrate biodiversity. Establishing sustained DNA-based monitoring, expanding reference databases, and integrating biodiversity data with functional ecosystem metrics will be critical to protecting Congaree’s belowground biodiversity and maintaining the park’s ecological resilience (Bardgett and van der Putten 2014; Lavelle et al. 1997; Lubbers et al. 2020).

Strategies for Mitigating Invasive Soil Invertebrates

Controlling invasive soil invertebrates is a daunting task since many species spread through human-mediated pathways and these invaders can affect ecosystem processes long before detection. For example, in north-temperate forests, exotic earthworm invasions can eliminate much of the organic forest floor thereby altering nutrient cycling and the composition of the soil biota, making “control” inseparable from prevention and containment (Bohlen et al. 2004). Invasive ants can likewise reach high densities quickly and disrupt ecological interactions across invaded habitats, therefore management often emphasizes sustained suppression rather than permanent elimination (Holway et al. 2002). For predatory soil arthropods such as introduced centipedes, habitat fragmentation and edge creation can increase invasion opportunities, implying that land-use planning could function as or at least contribute to a feasible control strategy (Hickerson et al. 2005). Across these groups, the literature repeatedly highlights early detection and the interruption of introduction pathways as a crucial first step since post-establishment eradication is limited technologically, logistically (e.g., large spatial scales), or simply impossible (Callaham et al. 2006; Chang et al. 2021).

Earthworm control in regions where soils and forests historically lacked earthworms is grounded in limiting introductions and secondary spread rather than relying on site-specific eradication. Documented introduction avenues include the fish-bait and horticulture industries, and policy documents emphasize regulation and educational outreach rather than large-scale elimination as viable options (Callaham et al. 2006). However, landscape features associated with disturbance can also structure invasion risk—in northern boreal forests, earthworm invasion patterns were strongly associated with road age, underscoring the importance of sanitation and risk reduction around transportation corridors and access routes (Cameron and Bayne 2009). The rapid spread of invasive “jumping worms” (genus Amynthas) has reinforced this prevention-first recommendation, with inspection, containment, and the minimization of potentially contaminated soil, mulch, and plants as practical near-term interventions (Chang et al. 2021). Because earthworms can be transported unintentionally in soil and organic media, behavioral changes such as bait disposal practices and careful movement of soils should be considered a core component of control alongside formal and practical policy tools (Callaham et al. 2006; Chang et al. 2021).

Where invasive earthworms have already established, control goals are often tied to mitigating impacts on forest-floor structure and understory biodiversity. Earthworm invasion can drive the dramatic loss of forest floor habitats and shift the depth and character of nutrient cycling, with cascading changes in soil carbon and nutrient pools (Bohlen et al. 2004). Consistent with these soil changes, hardwood forest understory plant communities have shown substantial compositional shifts (Hale et al. 2006), and plant species richness has been reported to decline in invaded northern hardwood forests (Holdsworth et al. 2007). Because invasions currently continue into previously earthworm-free temperate and boreal forests, management approaches often prioritize maintaining refugia for native species, slowing spread from disturbed entry points, and integrating earthworm risk into broader forest management decisions rather than attempting wholesale removal (Callaham et al. 2006; Frelich et al. 2006). A recent review that specifically focused on jumping worms emphasizes containment and spread reduction as the most realistic near-term strategies given the current state of control options (Chang et al. 2021).

For invasive ants, effective control depends on aligning management approaches with ant social organization and the spatial scale of ant colonies, because many invasive ant species form expansive, multi-queened networks that can quickly recolonize treated areas (e.g., Silverman and Brightwell 2008). More troubling, invading ants can quickly overwhelm local ecosystems and rapidly achieve high densities, displacing native ant species and thereby disrupting local ecological interactions. For this reason, invasive ant “control” frequently aims for sustained suppression over large areas rather than one-time elimination (Holway et al. 2002; Silverman and Brightwell 2008). This approach is consistent with pest management schemes that explicitly build monitoring and continued interventions into the program design, because colony networks extend beyond property boundaries and local gains can be offset by reinvasion from adjacent habitat (Drees et al. 2013; Silverman and Brightwell 2008).

Compared with ants and earthworms, invasive centipedes are less frequently the target of control programs, but the literature still suggests that introduction prevention, management of disturbances that favor exotics, and the conservation of native biotic resistance are viable management options. In temperate forest fragments, an introduced European centipede was most abundant in edge habitat, whereas a native centipede dominated interiors (Hickerson et al. 2005). Laboratory microcosm studies showed that native species could act as a predator on the introduced centipede, suggesting that maintaining intact interiors and native predator assemblages can reduce invasion pressure from edges (Hickerson et al. 2005). Most studies however frame invasive centipede mitigation as primarily educational and/or habitat-based prevention—limiting edge creation and preventing human-assisted introductions (Hickerson et al. 2005; Kamalakannan et al. 2025).

Strategies for Conserving Firefly Habitat

Despite the popularity of fireflies to the public and academic community, very little is known regarding the life-history of this group, particularly for individual species in the United States, and especially so for the larval stage (Riley et al. 2021b). Fireflies are thought to spend 1–2 years in the larval stage prior to mating and are varied in their habitat preferences. Some prefer terrestrial habitats (vegetation, rotting logs or under rocks and other debris) where others are partly or entirely fossorial although very little is known about the larval stages of many species (Riley 2021b). Conserving firefly habitat begins with protecting the full suite of environments required across life stages, because larval development and adult courtship often depend on different microhabitats and landscape features (Lewis et al. 2024). This life-history reality emphasizes the importance of sites that simultaneously support juvenile development, adult mating activity, and the prey resources that sustain larvae for months to years (Lewis et al. 2024; Riley et al. 2021b). IUCN-based assessments for North America indicate that threatened taxa are disproportionately associated with specialized habitats and have restricted ranges, underscoring the value of prioritizing habitat specialists for protection and restoration rather than assuming that general greenspace would be sufficient (Fallon et al. 2021). Accordingly, an evidence-based habitat mitigation strategy is to expand and connect protected areas (and, where relevant, establish buffer zones) specifically designed to safeguard resources needed by all life stages, and to restore degraded sites so that larval and adult requirements are co-supported in the same landscape (Fallon et al. 2021; Lewis et al. 2024).

Because moisture is repeatedly identified as a common requirement for fireflies, hydrology and microclimate management are crucial to comprehensive management strategies (Fallon et al. 2021; Lewis et al. 2024). Moisture helps prevent desiccation of eggs and soft-bodied immature stages and supports the persistence of soft-bodied prey, meaning that droughts or water mismanagement can negatively impact suitable firefly habitat (Fallon et al. 2021). Practical management approaches therefore include maintaining natural water-flow regimes (e.g., avoiding drainage, groundwater drawdown, or flow interruption) and the retention or restoration of riparian and wetland vegetation that stabilizes humidity and soil moisture (Fallon et al. 2021; Lewis et al. 2024). Where conservation planning is formalized, explicitly including moisture retention and wetland/riparian integrity as conservation objectives aligns with evidence that many at-risk species are tied to specialized moist habitats (Fallon et al. 2021; Lewis et al. 2024).

Artificial light is likewise a habitat-quality issue for fireflies, since many species rely on courtship signals that are sensitive to ambient light conditions (Owens et al. 2022). Manipulative field experiments demonstrate that added nighttime lighting can substantially reduce flashing activity and depress courtship behavior and mating success in multiple species, directly linking artificial light to reduced reproductive performance (Firebaugh and Haynes 2016; Owens and Lewis 2022). Conservation-based lighting strategies might include minimizing light intrusion into breeding habitat (through shielding and directed fixtures), reducing intensity and duration (dimming, curfews, and motion activation), and managing spectral composition to avoid broad-spectrum lighting (Lewis et al. 2024; Owens et al. 2022). Experimental work shows that long-wavelength ambient light had comparatively little effect on key firefly signal parameters in one species, supporting the habitat strategy of favoring longer-wavelength lighting in or near sensitive areas when lighting is unavoidable (Owens et al. 2018).

Reducing pesticide exposure is another habitat-conservation priority because firefly larvae develop within substrates and prey on soft-bodied invertebrates that can be contaminated by insecticides, and because larval prey can serve as an exposure pathway (Lewis et al. 2024). Laboratory toxicity assays have shown that pesticides such as clothianidin can harm common Eastern North American fireflies, providing direct evidence that soil-associated insecticides pose risks to firefly persistence in managed landscapes (Pearsons et al. 2021). Translating this evidence into habitat strategy implies eliminating or sharply limiting broad-spectrum insecticide use in firefly habitat and adopting integrated pest management approaches that reduce preventive applications, particularly in habitats where fireflies reproduce (Lewis et al. 2024). Because larval fireflies are predaceous and depend on soft-bodied invertebrate prey, conserving habitat also includes maintaining an uncontaminated prey base and avoiding management actions that are detrimental to the local invertebrate community supporting larval growth (Lewis et al. 2024; Pearsons et al. 2021).

Finally, habitat conservation strategies should address recreation pressure and monitoring, especially where charismatic mating displays attract visitors and can intensify localized disturbance (Faust 2010; Lewis et al. 2024). Protecting fragile wetland and riparian habitats from trampling using measures such as boardwalks or fencing and ensuring that protected-area management plans explicitly include actions to reduce stray broad-spectrum light and insecticide exposure (Lewis et al. 2024). Because many species remain data deficient, conservation strategy also includes building monitoring capacity (including community science approaches), so that habitat interventions can be evaluated without unnecessarily harming localized populations (Fallon et al. 2021; Lewis et al. 2024).

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Appendix

Table 6 provides a taxonomic inventory of organisms identified by DNA barcoding and Shannon diversity across sampling locations in Congaree National Park. See Table 1 for a description and location of sampling locations.

Table 6. Taxonomic inventory and counts (in parentheses—values are the number of individuals identified by COI barcoding for each taxon) across sampling sites in Congaree National Park. Abbreviations: #S = soil sample, #L = litter sample, N/A = individuals with PI scores <90% and/or QC scores of <95% that were not assigned, H = Shannon Diversity for each site. Taxonomic names are sorted alphabetically within sites and not by taxonomic group.
Site Taxon Count H
1L Datana contracta 1 1.386
1L Gnaphosidae 1 1.386
1L Neoscona sp. 1 1.386
1L Scolopocryptops nigridius 1 1.386
1L Trichonephila 1 1.386
1S Amynthas corticis 1 0.693
1S Efferia aestuans 1 0.693
1S N/A 1 0.693
2aL Amynthas corticis 3 2.098
2aL Apache sp. 1 2.098
2aL Balta sp. 1 2.098
2aL Chinattus parvulus 2 2.098
2aL Euborellia arcanum 1 2.098
2aL Glyphyalinia indentata 1 2.098
2aL Laccophilus sp. 1 2.098
2aL Lithobius sp. 3 2.098
2aL Photuris cf. tremulans 4 2.098
2aL Scolopocryptops nigridius 1 2.098
2aL Stratiomyidae sp. 8 2.098
2aL N/A 9 2.098
2bL Abacidus cf. permundus 1 1.733
2bL Brachyponera chinensis 2 1.733
2bL Paria fragariae 1 1.733
2bL Prosapia 1 1.733
2bL Schizocosa ocreata 2 1.733
2bL Tigrosa aspersa 1 1.733
2bL N/A 2 1.733
2aS Eisenoides cf. lonnbergi 4 1.154
2aS Geophilus sp. 1 1.154
2aS Metaphire californica 1 1.154
2aS Narceus cf. annularus 1 1.154
2aS N/A 3 1.154
3L Amynthas corticis 7 2.482
3L Amynthas gracilis 9 2.482
3L Cyclosa sp. 1 2.482
3L Ectobiidae sp. 1 2.482
3L Epiphragma sp. 1 2.482
3L Geophilus sp. 1 2.482
3L Gladicosa bellamyi 1 2.482
3L Harpalus compar 2 2.482
3L Hypoponera opaciceps 1 2.482
3L Isoxya sp. 1 2.482
3L Macaria aemulataria 2 2.482
3L Nadata sp. 1 2.482
3L Narceus cf. annularus 1 2.482
3L Pinophilus sp. 1 2.482
3L Pisaurina mira 1 2.482
3L Platydracus cinnamopterus 1 2.482
3L Uloma sp. 2 2.482
3L Xysticus ferox 1 2.482
3L N/A 3 2.482
3S Amynthas corticis 3 2.057
3S Amynthas gracilis 8 2.057
3S Amynthas hupeiensis 1 2.057
3S Amynthas minimus 3 2.057
3S Callistethus granulipygus 3 2.057
3S Diplocardia caroliniana 1 2.057
3S Diplotaxis sp. 2 2.057
3S Lasius alienus 4 2.057
3S Octolasion cyaneum 1 2.057
3S Trichotichnus sp. 1 2.057
3S N/A 2 2.057
4L Allocosa senex 2 2.938
4L Amynthas corticis 2 2.938
4L Amynthas gracilis 5 2.938
4L Aphaenogaster fulva 17 2.938
4L Bombycidae 1 2.938
4L Callistethus granulipygus 1 2.938
4L Camponotus pennsylvanicus 1 2.938
4L Chlaenius cf. sericeus 1 2.938
4L Chrodeumatida sp. 1 2.938
4L Dicaelus cf. elongatus 1 2.938
4L Elater sp. 1 2.938
4L Harpalus compar 2 2.938
4L Hemiscolopendra marginata 1 2.938
4L Hybomitra cf. polaris 1 2.938
4L Lithobius sp. 1 2.938
4L Macrodactylus subspinosus 2 2.938
4L Mangora maculata 1 2.938
4L Megapallifera ragsdalei 1 2.938
4L Metaphire californica 2 2.938
4L Myrmecina americana 7 2.938
4L Neandra brunnea 1 2.938
4L Nyssomyia sp. 1 2.938
4L Parcoblatta sp. 1 2.938
4L Photuris cf. tremulans 2 2.938
4L Photuris quadrifulgens 1 2.938
4L Phyllophaga sp. 1 2.938
4L Schizocosa crassipes 1 2.938
4L Strigamia sp. 1 2.938
4L Tabanus pallidescens 1 2.938
4L Trichotichnus sp. 1 2.938
4L Trombidiidae 1 2.938
4L Velarifictorus micado 1 2.938
4L N/A 22 2.938
4S Amynthas minimus 2 1.352
4S Brachymyrmex depilis 2 1.352
4S Callistethus granulipygus 2 1.352
4S Popillia japonica 1 1.352
4S N/A 2 1.352
5L Solenopsis invicta 3 0.000
5L N/A 2 0.000
6L Amynthas corticis 5 1.584
6L Germarostes globosus 1 1.584
6L Lithobius sp. 1 1.584
6L Odontotaenius disjunctus 12 1.584
6L Oxidus gracilis 8 1.584
6L Sciaridae 1 1.584
6L Solenopsis invicta 1 1.584
6L Trichrysis kylan 1 1.584
6L N/A 21 1.584
6S Amynthas corticis 5 1.707
6S Amynthas gracilis 4 1.707
6S Amynthas minimus 1 1.707
6S Diplocardia singularis 1 1.707
6S Metaphire californica 1 1.707
6S Oxidus gracilis 4 1.707
6S Uloma sp. 1 1.707
6S N/A 6 1.707
7L Hypagyrtis unipunctata 1 1.303
7L Neoantistea agilis 1 1.303
7L Reticulitermes flavipes 5 1.303
7L Schizocosa crassipes 1 1.303
7L Tigrosa sp. 1 1.303
7L N/A 2 1.303
7S Amynthas corticis 1 1.991
7S Aphaenogaster carolinensis 6 1.991
7S Aphaenogaster rudis 1 1.991
7S Brachinus sp. 1 1.991
7S Japygidae 1 1.991
7S Pheidole sp. 1 1.991
7S Phytocoris eximius 1 1.991
7S Sciara sp. 1 1.991
7S Serica intermixta 1 1.991
7S Xysticus ferox 1 1.991
7S N/A 1 1.991
8L Formica subaenescens 6 0.000
8S Prenolepis imparis 1 0.000
9L Anahita sp. 1 2.079
9L Aphaenogaster carolinensis 1 2.079
9L Dolomedes sp. 1 2.079
9L Eremocoris sp. 1 2.079
9L Leiobunum sp. 1 2.079
9L Oxycrepis velocipes 1 2.079
9L Rhabdoblatta sp. 1 2.079
9L Stratiomyidae sp. 1 2.079
9L N/A 2 2.079
9S Aphaenogaster carolinensis 1 0.000
9S N/A 2 0.000
10L Castianeira cf. gertschi 1 1.303
10L Ochlerotatus sticticus 1 1.303
10L Phrurotimpus sp. 1 1.303
10L Solenopsis invicta 5 1.303
10L Tomocerus sp. 1 1.303
10L N/A 5 1.303
10S Strigamia sp. 1 0.000
10S N/A 2 0.000
11L Balta sp. 1 1.332
11L Mangora gibberosa 1 1.332
11L Solenopsis invicta 2 1.332
11L Theridion pennsylvanicum 1 1.332
11S Anomala sp. 1 0.000
11S Neocicada hieroglyphica 1 0.000
12L Clubiona abboti 1 1.792
12L Pseudomyrmex pallidus 1 1.792
12L Schizocosa crassipes 1 1.792
12L Solenopsis invicta 1 1.792
12L Stratiomyidae sp. 1 1.792
12L Tomocerus sp. 1 1.792
12L N/A 2 1.792
12S Diplocardia sp. 1 0.000
12S N/A 2 0.000
13L Eremocoris sp. 1 1.792
13L Formica schaufussi (= F. pallidefulva) 1 1.792
13L Melanolestes cf. picipes 1 1.792
13L Neoantistea gosiuta 1 1.792
13L Rhabdoblatta sp. 1 1.792
13L Schizocosa rovneri 1 1.792
13S Bimastos sp. 3 0.562
13S Metaphire sp. 1 0.562
13S N/A 2 0.562
14L Glenognatha cf. foxi 1 0.693
14L Solenopsis invicta 1 0.693
14S Amynthas sp. 1 1.099
14S Dendrobaena hortensis 1 1.099
14S Diplocardia sp. 1 1.099
14S N/A 1 1.099
15L Aphaenogaster sp. 1 1.609
15L Megapallifera ragsdalei 1 1.609
15L Neoantistea agilis 1 1.609
15L Schizocosa ocreata 1 1.609
15L Tigrosa sp. 1 1.609
15L N/A 5 1.609
15S Prenolepis imparis 3 0.950
15S Scolopocryptops nigridius 1 0.950
15S Sigmoria sp. 1 0.950
15S N/A 3 0.950
16L Dolomedes tenebrosus 1 0.693
16L Mangora maculata 1 0.693
16S Dendrobaena sp. 1 0.000
17L Agonum moerens 1 0.693
17L Arugisa sp. 1 0.693
17L N/A 3 0.693
17S Aphaenogaster carolinensis 3 0.562
17S Camponotus chromaiodes 1 0.562
17S N/A 3 0.562
18L Amynthas corticis 1 1.946
18L Galerita janus 1 1.946
18L Schizocosa sp. 1 1.946
18L Schizocosa ocreata 1 1.946
18L Scolopocryptops nigridius 1 1.946
18L Staphylinidae 1 1.946
18L Velarifictorus sp. 1 1.946
18L N/A 3 1.946
18S Callistethus sp. 2 1.332
18S Cyclotrachelus sigillatus 1 1.332
18S Prenolepis imparis 1 1.332
18S Psilocorsis reflexella 1 1.332
18S N/A 5 1.332
19L Castianeira cf. gertschi 1 1.099
19L Myrmecina americana 1 1.099
19L Pirata minutus 1 1.099
19L N/A 3 1.099
19S Amara aenea 1 1.099
19S Stratiomyidae sp. 1 1.099
19S Uloma sp. 1 1.099
19S N/A 2 1.099
20L Parcoblatta pensylvanica 1 0.796
20L Parcoblatta sp. 1 0.796
20L Solenopsis invicta 5 0.796
20L N/A 2 0.796
20S Curculio cf. sulcatulus 1 1.475
20S Diplocardia sp. 3 1.475
20S Drassyllus eremitus 1 1.475
20S Euphoria fulgida 1 1.475
20S Neoantistea gosiuta 1 1.475
20S N/A 2 1.475
21L Eisenoides cf. lonnbergi 1 1.332
21L Ischnobaenella sp. 1 1.332
21L Salticidae 1 1.332
21L Schizocosa ocreata 2 1.332
21L N/A 2 1.332
21S Amynthas corticis 1 1.099
21S Dendrobaena attemsi 1 1.099
21S Eisenoides cf. lonnbergi 1 1.099
21S N/A 4 1.099
22L Amynthas corticis 1 0.693
22L Camponotus castaneus 1 0.693
22L N/A 4 0.693
23L Crematogaster ashmeadi 1 1.099
23L Rhabdoblatta sp. 1 1.099
23L Schizocosa sp. 1 1.099
23L N/A 1 1.099
23S Callistethus granulipygus 2 1.889
23S Hebardina sp. 1 1.889
23S Phyllophaga rugosa 1 1.889
23S Schizocosa ocreata 1 1.889
23S Scolopocryptops nigridius 1 1.889
23S Serica intermixta 2 1.889
23S Strigamia sp. 1 1.889
24L Balta sp. 1 1.386
24L Brachyponera chinensis 1 1.386
24L Drassyllus eremitus 1 1.386
24L Leucauge argyrobapta 1 1.386
24L N/A 7 1.386
24S Amynthas gracilis 1 1.099
24S Melanotus hyslopi 1 1.099
24S Phyllophaga sp. 1 1.099
24S N/A 1 1.099
25L Hymenorus sp. 1 1.609
25L Narceus cf. annularus 1 1.609
25L Neoantistea agilis 1 1.609
25L Schizocosa ocreata 1 1.609
25L Sigmoria sp. 2 1.609
25L N/A 8 1.609
25S Amynthas corticis 2 1.523
25S Amynthas gracilis 3 1.523
25S Callistethus sp. 2 1.523
25S Melanotus morosus 1 1.523
25S Phyllophaga hirticula 1 1.523
25S N/A 10 1.523
26aL Apantesis parthenice 1 2.438
26aL Brachyponera chinensis 2 2.438
26aL Camponotus pennsylvanicus 1 2.438
26aL Gladicosa bellamyi 1 2.438
26aL Harmonia axyridis 1 2.438
26aL Malacosoma disstria 5 2.438
26aL Neoantistea agilis 3 2.438
26aL Neoantistea gosiuta 1 2.438
26aL Oscheius tipulae 1 2.438
26aL Phrurotimpus sp. 1 2.438
26aL Rhagonycha recta 1 2.438
26aL Schizocosa ocreata 2 2.438
26aL Schizocosa rovneri 1 2.438
26aL Tomoceridae 1 2.438
26aL N/A 6 2.438
26bL Bathyphantes pallidus 1 1.906
26bL Brachyponera chinensis 2 1.906
26bL Dolomedes tenebrosus 1 1.906
26bL Eunemobius cf. carolinus 1 1.906
26bL Mesodon cf. thyroidus 1 1.906
26bL Oxidus gracilis 1 1.906
26bL Pseudopolydesmus sp. 2 1.906
26bL N/A 8 1.906
26aS Eisenoides cf. lonnbergi 2 0.637
26aS Phyllophaga ephilida 1 0.637
27L Agonum decorum 1 2.232
27L Amynthas corticis 1 2.232
27L Armadillidium nasatum 2 2.232
27L Balta sp. 1 2.232
27L Brachyponera chinensis 4 2.232
27L Camponotus castaneus 1 2.232
27L Mangora maculata 1 2.232
27L Mutillidae 1 2.232
27L Oxidus gracilis 3 2.232
27L Schizocosa ocreata 1 2.232
27L Ventridens intertextus 1 2.232
27L N/A 7 2.232
27S Amynthas gracilis 1 1.386
27S Amynthas minimus 1 1.386
27S Brachyponera chinensis 1 1.386
27S Oxidus gracilis 1 1.386
27S N/A 7 1.386
28L Castianeira cf. variata 1 1.561
28L Mimosilpha sp. 1 1.561
28L Pheidole dentata 1 1.561
28L Sigmoria latior hoffmani 2 1.561
28L Uloma sp. 1 1.561
28L N/A 1 1.561
29L Hypercompe scribonia 1 0.693
29L Hypsosinga rubens 1 0.693
29L N/A 1 0.693
29S Drassyllus niger 1 0.693
29S Pelochrista comatulana 1 0.693
29S N/A 2 0.693
30L Bimastos sp. 2 2.023
30L Cyclotrachelus sigillatus 2 2.023
30L Piratula minuta 2 2.023
30L Schizocosa crassipes 1 2.023
30L Scolopocryptops nigridius 1 2.023
30L Sigmoria sp. 1 2.023
30L Strigamia sp. 1 2.023
30L Varacosa avara 2 2.023
30L N/A 5 2.023
30S Dendrobaena sp. 2 0.637
30S Serica intermixta 1 0.637
30S N/A 3 0.637
31L Abacion sp. 2 2.146
31L Amynthas corticis 1 2.146
31L Drassyllus eremitus 1 2.146
31L Eisenoides cf. lonnbergi 2 2.146
31L Hypercompe scribonia 1 2.146
31L Metasarcus sp. 1 2.146
31L Narceus cf. annularus 1 2.146
31L Pseudolathra sp. 1 2.146
31L Sigmoria sp. 1 2.146
31L N/A 3 2.146
31S Amynthas corticis 2 0.000
31S N/A 3 0.000
32L Brachyponera chinensis 1 2.548
32L Chinattus cf. parvulus 1 2.548
32L Cyclotrachelus substriatus 1 2.548
32L Drassyllus niger 1 2.548
32L Geophilus sp. 1 2.548
32L Odontotaenius disjunctus 1 2.548
32L Photuris cf. lucicrescens 1 2.548
32L Phrurotimpus borealis 1 2.548
32L Reticulitermes flavipes 1 2.548
32L Stratiomyidae 1 2.548
32L Tigrosa aspersa 1 2.548
32L Uloma impressa 2 2.548
32L N/A 2 2.548
33L Gasteracantha cancriformis 1 1.386
33L Sigmoria sp. 1 1.386
33L Tigrosa aspersa 1 1.386
33L Ventridens ligera 1 1.386
33L N/A 1 1.386
34L Megapallifera ragsdalei 1 0.693
34L Ventridens intertextus 1 0.693
35L Euborellia arcanum 3 1.475
35L Hymenorus dubius 1 1.475
35L Scolopocryptops nigridius 1 1.475
35L Strigamia acuminata 1 1.475
35L Tigrosa aspersa 1 1.475
35L N/A 6 1.475
36L Parcoblatta pensylvanica 1 1.386
36L Synuchus sp. 1 1.386
36L Uloma impressa 1 1.386
36L Zonitoides arboreus 1 1.386
36L N/A 5 1.386
37L Haplotrema sp. 1 0.693
37L Hypoponera sp. 1 0.693
37L N/A 1 0.693
38L Brachyponera chinensis 4 1.907
38L Dipropus sp. 1 1.907
38L Gasteracantha cancriformis 1 1.907
38L Hymenorus dubius 1 1.907
38L Megapallifera ragsdalei 1 1.907
38L Neriene radiata 1 1.907
38L Parcoblatta pensylvanica 1 1.907
38L Zonitoides arboreus 2 1.907
38L N/A 3 1.907
39L N/A 1 0.000

Author Information

Joseph M. Quattro

Sharon A. Kendrick

University of South Carolina
Department of Biological Sciences
715 Sumter St.
Columbia, SC 29208

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