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Ground-dwelling arthropod association with coarse woody debris following long-term dormant season prescribed burning in the longleaf pine flatwoods of north Florida.

Large dead wood in terrestrial forest habitats is an important resource for a number of arthropods and other animals that use it for food, oviposition sites, protection from environmental extremes, and foraging habitat (Elton 1966; Harmon et al. 1986; Grove 2002). Within this habitat saproxylic arthropod communities vary with species of tree, stage of decomposition, types of fungal colonists, and location (aquatic to xeric) of dead wood in the landscape (Harmon et al. 1986; Speight 1989; Okland et al. 1996; Grove 2002). In addition to direct contribution to forest diversity, saproxylic arthropods are an important part of the food web supporting a variety of predators and parasites (Harmon et al. 1986). Considerable research on coarse woody debris and its function in forests has been done in North America but most of the emphasis has been in the Pacific Northwest and Canada (Harmon et al. 1986; Woldendorp et al. 2002). Within North America there is a large body of literature on the Scolytinae (Curculionidae: Coleoptera) and their associates but relatively little work on other saproxylic species or those that occur in later stages of decay (Savely 1939; Howden & Vogt 1951; Hammond et al. 2001, 2004), and less is known about how dead wood affects the distribution of arthropods that may be associated with it but not totally dependent on it (Irmler et al. 1996; Marra & Edmonds 1998; Andrew et al. 2000; Buddle 2001). Despite the growing interest in this topic, interactions between woody debris and arthropod communities in the Southeastern United States have received little attention (McMinn & Crossley 1996) and this is particularly true for longleaf pine (Pinus palustris) forests.

Longleaf pine once occupied about 30 million hectares in the Southeastern region of North America (Frost 1993). Today <1.3 million hectares remain as small, isolated parcels (Outcalt & Sheffield 1996) resulting in longleaf pine and associated communities being classified as the third most endangered ecosystem in the United States (Noss et al. 1995). Conservation and restoration of these communities is a priority for forest management and conservation groups throughout the region (Van Lear et al. 2005).

Prior to European arrival in North America, longleaf pine communities were maintained by frequent fires (1-3 year intervals) started by lightning or Native Americans (Landers 1991) but this is no longer the norm. An unanticipated result of reduced fire frequency is the increased accumulation of fuels which foster fires that are significantly more likely to damage both the environment and stand improvements, and pose a much greater threat to human health and safety (Oultcalt & Wade 2004). A number of remedies are available and, although prescribed burning is the only alternative that addresses the full suite of ecosystem components, other alternatives are commonly chosen to achieve short-term results (Van Lear et al. 2005). Even when fire is used, these remaining longleaf pine communities are often not managed under the appropriate fire regime (Brose & Wade 2002).

A number of studies and reviews have looked at effects of fire on arthropods but no clear general trends have emerged (Rice 1932; Heyward & Tissot 1936; Pearse 1943; Buffington 1967; Ahlgren 1974; Hurst 1971; Warren et al. 1987; Muona & Rutanen 1994; Buddle et al. 2000; Niwa & Peck 2002; Hanula & Wade 2003). However, despite the lack of a general trend, fire has a significant effect on many epigaeic arthropods and this is particularly true for the longleaf pine flatwoods ecosystem where it reduces diversity, community similarity and populations of many taxa (Hanula & Wade 2003). In addition to direct mortality, fire alters the forest habitat and community structure resulting in either positive or negative effects on a given species. For example, by reducing predators (spiders, centipedes, ground beetles, etc.) some species may experience population increases or, conversely, reductions in prey may reduce predator populations. Fire may also reduce food for detritivores (e.g., termites) and cover for other species. By removing refuges such as litter or low growing shrubs, fire may increase susceptibility to predation, temperature extremes or desiccation.

Elton (1966) recognized the importance of woody debris as a source of forest diversity and he noted that as wood decomposes it is increasingly colonized by generalists that do not require specific tree species or even depend on woody debris as their sole habitat. For example, Irmler et al. (1996) found that the increasing variety of wood-dwelling species as dead wood aged was due largely to immigration of litter dwelling species to dead wood and to species using dead wood for overwintering. Likewise, Cercamo & Parkinson (1999) reported that decomposed coarse woody debris was a major factor in shaping ground beetle assemblages, and Evans et al. (2003) and Jabin et al. (2004) reported that proximity to logs was an important factor in shaping litter-dwelling invertebrate communities. These studies demonstrate the role dead wood can play in shaping epigaeic arthropod communities, which can in turn affect a variety of ecological processes (Evans et al. 2003; Jabin et al. 2004).

While planning a study on the long-term effects of frequent dormant-season burning on epigaeic arthropods (Hanula & Wade 2003) we hypothesized that the presence of coarse woody debris would be more important to these arthropods on annually burned plots where much of the leaf litter, understory vegetation and structure is sparse and has little time to recover between burns. To address this hypothesis, we conducted a study over a 5-year period to examine whether the presence of woody debris influenced pitfall trap captures under varying burn frequencies in a longleaf pine forest. In addition, we measured the amount of coarse woody debris and other habitat variables to determine how long-term frequent burning interacted with these variables to shape the epigaeic arthropod community.


Study Site and Prescribed Burn Treatments

The study was conducted in the Osceola National Forest in Baker County, Florida. Study plots were established in 1958 to examine effects of burning frequency on fuel reduction for wildfire prevention. At that time the overstory trees were 45-year-old longleaf pines that were 20 m tall and 29 cm diameter (DBH) with a few slash pine (P. elliottii) of similar size. The presence of remnant "boxed" longleaf pine trees from past turpentine operations suggest that the area was never cleared for agriculture. The understory consists of typical flatwoods vegetation dominated by saw palmetto (Serenoa repens), gallberry (Ilex glabra), Vaccinium sp. and wiregrass (Aristida beyrichiana). The study was a randomized complete block design consisting of 24, 0.8-ha plots arranged in 6 blocks of 4 treatments. Initial treatments were winter burns applied every 2, 4 or 6 years and unburned controls. However, in 1964 the 6-year interval treatment was replaced with annual winter burns. The entire site was burned in 1958 to initialize the study area, and treatments have been applied as scheduled since then between Dec and early Mar. Fire intensity varied from year to year depending on weather conditions, dead fine-fuel moisture content (which typically ranged from 7-20%), and firing techniques. Our study, superimposed on this long-term winter burn study, started in fall, 1994 just before the application of annual burns in the winter of 19941995. The study continued for the full cycle of burn treatments (annual, biennial, and quadrennial) ending after the quadrennial plots were again burned the winter of 1999-2000. Control plots had been unburned for 42 years at the close of our study.

We sampled the naturally occurring large dead wood on the plots in Dec 2003, three years after the study. Small dead wood was not affected by fire frequency on our plots (Hanula & Wade 2003), so it is unlikely that large dead wood was, and there was no evidence of increased tree mortality following our study so the delay in measuring this attribute did not result in significant changes in dead wood volumes between the time insects and dead wood were sampled. We estimated large dead wood volume by measuring the end diameters of all down woody debris in five 10-m wide transects which resulted in sampling approximately 50% of the plot surface. In addition, we conducted a 100% survey of standing dead wood. We used Huber's equation (volume = m x l; where, m = mid-point cross-sectional area and l = length) to estimate volume of dead wood (Avery 1975). The mid-point diameter of downed wood was estimated by taking the average of the end diameters. The mid-point diameter of standing dead wood was estimated using taper equations for coastal plain longleaf pine (Clark et al. 1991).

Pre-burn live and dead plant biomass (dried) was estimated on each plot from eight 1-[m.sup.2] subplots. Details of sampling procedures for live and dead plant material can be found in Hanula & Wade (2003). Sampling methods were adapted from Shea et al. (1996). Two clusters of 8 transect lines were established in each plot. We ran 15 m long transects from the center of each cluster in the 4 cardinal directions and 4 additional 15-m long transects were established at 90[degrees] from the ends of the original transects. Sampling frames (1 [m.sup.2]) were placed on the ground 4 m from the starting point of each transect. All stems <1.9 cm basal diameter were collected by category from each sample point, bagged, oven-dried at 42[degrees]C for a minimum of 48 h, and weighed. Plant biomass was separated into 11 categories consisting of (1) live palmetto, (2) dead palmetto, (3) live gallberry, (4) live pine needles (seedlings), (5) live grasses and forbs, (6) litter layer (O1 or L layer), (7) humus (O2 and O3 or F and H layers), (8) pine cones, (9) 0-0.6 cm dead branches, (10) 0.6-2.5 cm dead branches, and (11) other dead woody material.

Arthropod Sampling

We hypothesized that logs could function in two ways to increase trap captures of arthropods: (1) they could be a preferred habitat resulting in concentrations of arthropods around them, or (2) they could act as drift fences concentrating and directing arthropods normally wandering across the forest floor into traps near them. To determine if large dead wood was a preferred habitat or acted as a drift fence, we placed 3-m long logs of longleaf pine (20-25 cm diameter) in the center of each plot and installed pitfall traps along them immediately after felling. Four pitfall traps constructed from 480-mL capacity plastic cups (Hanula & Franzreb 1998) were installed near the log at plot center (2 on each side 0.5 m from each end) as close to the log as possible. To decrease the chances of an arthropod bypassing the pitfall by moving between the log and the pitfall we inserted wedge-shaped pieces of aluminum sheet metal into the space so that the metal sheeting created a barrier from the point where the log contacted the ground to the edge of the pitfall. A second set of 4 pitfall traps was installed along a 3-m long aluminum sheet metal drift fence (15 cm high). The drift fence was located 10 m from the center log. Traps were constructed and placed in the same manner as the log pitfalls except that the edge of each trap was in direct contact with the drift fence. Beginning in Jan 1995, pitfall traps were opened for month-long periods 6 times per year and covered with ceramic tiles during months when not in use to prevent arthropods from falling in. Arthropods collected in this way were placed in 70% ethyl alcohol, sorted to morphologically similar groups, and identified to genus or the lowest taxonomic level possible by using a reference collection and appropriate taxonomic keys. Biomass estimates were obtained by oven drying (40 [degrees]C for 72 h) and weighing at least 20-30 specimens of each taxon. The average weight of these specimens was multiplied by the number of individuals within a sample to estimate sample biomass.

Statistical Analyses

Horn's simplification of Morisita's index of similarity was used to compare arthropod communities captured in the 2 types of traps (Horn 1966). We used richness and the Shannon diversity index to measure arthropod community diversity. Evenness was calculated to give further information about the arthropod communities among burn treatments. Calculations were based on the cumulative totals of each arthropod for each plot for the entire study period.

Because 2 trap locations were designated within each plot, the study was analyzed as a split-plot design. Analyses of variance were conducted with the SAS GLM procedure (SAS 1985) to test for interactions of burn frequency and trap location, and trap location effects. Model effects were burn treatment, trap location, block, treatment x block interaction, and burn treatment x trap location interaction. Analyses were conducted for taxa in which 30 or more individuals were caught during the entire study. Digweed et al. (1995) found evidence of depletion of certain Carabidae when traps were 10 m apart, so we also plotted annual captures of some common families and species to determine if there was any depletion effect from trapping 4 years at the same location or from drift fence pitfalls being 10 m from the pitfalls near logs.

In order to examine arthropod community responses, we used non-metric multidimensional scaling analysis (NMDS) of trends in arthropod abundance in the study plots. We chose NMDS because there appeared to be an "arch effect" in a preliminary canonical correspondence analysis and because this method is more robust to variability in underlying patterns in morphospecies responses than are eigenvalue-based ordination techniques (Clarke 1993; Gaiser et al. 1998). Morphospecies that had less than 10 individuals were excluded resulting in a total of 184 morphospecies in the analysis. To examine responses of the arthropod community to environmental variables, we analyzed the relationship between the NMDS scores and environmental variables using vector analysis (Gaiser et al. 1998). Vector analysis, a form of indirect gradient analysis, is a regression of the environmental variables on the NMDS scores with the results displayed as vectors overlaid on a plot of NMDS scores. Because we were interested in coarse woody debris effects on arthropod community structure, and because fire frequency had such a strong effect on several variables, we excluded those environmental measures that were highly correlated to fire frequency (Table 1). We included fire frequency, gallberry biomass, small woody debris, downed coarse woody debris, and standing woody debris as the environmental variables in the vector analysis.


We caught significantly more total arthropods and a greater biomass of arthropods in pitfall traps near drift fences than in those near logs (Table 2). The similarity of what was caught in the 2 types of traps ranged from 64.4% (SE = 3.6) in the annually burned plots to 69.2% (SE = 1.4%) in the quadrennially burned plots, but there were no significant differences in similarity among burn treatments. Likewise, Shannon diversity, evenness, richness, and numbers of rare species were not significantly different for traps regardless of adjacent barrier.

We observed significant (P < 0.05) interactions between fire and trap location in 31 taxa (Table 3). Of those, 10 taxa had higher numbers captured in traps nears logs in some burn treatments (Fig. 1). However, there was no consistent pattern, i.e., in some cases frequent burning resulted in more of a given taxa near logs while infrequent or no burning had the same result for other taxa. In only a few cases did frequent burning result in concentrations of arthropods near logs, despite much lower leaf litter, less live herbaceous vegetation biomass, and reduced plant structure on those plots compared to unburned or quadrennially burned plots (Hanula & Wade 2003). Total volumes of coarse woody debris among burn treatments were not significantly different and ranged from a mean of 8.0 m3/ha (SE = 1.01) for unburned control plots to 9.1 m3/ha (SE = 1.73) on annually burned plots.

The Monte Carlo test results of the NMDS indicated a two-dimensional solution was optimal (final stress 11.73, P = 0.0323). The first 2 axes of the NMDS ordination explained 91.4% of the original variance, with the first axis explaining 69.6% and the second axis explaining an additional 21.8% (Fig. 2). The vector analysis indicated that fire frequency was correlated with both axes, more so with Axis 1 ([R.sup.2] = 0.678, P < 0.05) than Axis 2 ([R.sup.2] = 0.574, P < 0.05). DCWD was also significantly correlated to Axis 1 ([R.sup.2] = 0.197, P < 0.05) but not Axis 2. Other environmental variables were not correlated at [alpha] = 0.05.

We captured over 932 genera in 5 years of trapping of which 297 were captured in sufficient numbers for analysis. Of those, 101 arthropod taxa were captured in significantly higher numbers in one trap type or the other (Table 3). When examining this many individual taxa, one is certain to encounter some significant results simply by chance. However, the 101 taxa represent 34% of the total number of arthropod taxa with 30 or more individuals. At ? = 0.05 one would only expect 5% to have been captured in statistically higher numbers by chance. Seventy three different arthropods were captured in significantly (P < 0.05) higher numbers in pitfalls near drift fences while 28 different arthropods were captured in higher numbers near logs. We plotted the numbers captured by year to see if there was evidence of depletion from pitfall trapping for a long period of time or competition between traps. Fig. 3 shows 6 examples of those plots. Four years of trapping did not affect the abundance of arthropods in the vicinity of our traps nor did we see evidence of declining trap captures in one trap type as the other increased.


Prescribed burning over a 40-year period significantly changed the understory vegetation and litter layer of the study plots (Hanula & Wade 2003; Glitzenstein et al. 2003). Despite reduced litter and less shrub cover on annually burned plots, we saw no increased use of logs as habitat by ground dwelling arthropods. Andrew et al. (2000) reported similar results for ants although they suggested that a few rare species may benefit from the presence of logs in areas where frequent low intensity fires are commonly used. We found community similarity, diversity, and richness were the same for the 2 trap locations regardless of the frequency of winter burning. Likewise, we saw no increase in overall arthropod abundance or biomass near logs but instead we captured more in traps near metal drift fences. Habitat heterogeneity and structural diversity are considered important factors in determining community richness and diversity (Hutchinson 1959; Southwood et al. 1979; Tilman & Pacala 1993; Wright et al. 1993). However, in our study added logs did not result in greater overall abundance or diversity in their vicinity, despite annual burning on some plots, which greatly simplified the structure of the habitat for arthropods (Hanula & Wade 2003).



Arthropod community structure was driven by fire frequency and reported by Hanula & Wade (2003). The first axis of the NMDS ordination that explained almost 70% of the variance was highly correlated to fire return interval ([R.sup.2] = 0.678). Downed coarse woody debris did have some impact on overall insect community structure but the effect was much weaker than fire frequency ([R.sup.2] = 0.197). Possibly, the small differences in the volume of woody debris on the plots were insufficient to affect the arthropod community or the study time frame was too short (5 years). The results of Gunnarsson et al. (2004) suggest that the former may be the case. They found that diversity and abundance of ground-active beetles were related to the size of slash piles left after harvesting. Thus, larger amounts of dead wood in a given location may result in greater diversity of ground-dwelling arthropods in the vicinity. Gunnarsson et al. (2004) also point out that pitfall traps measure arthropod activity and not necessarily abundance. In contrast, Jabin et al. (2004) examined soil arthropods in litter samples near logs and distant from them in forests that contained about the same volume of dead wood as our study area. They found that CWD was an important structural component to a variety of saprophagous species regardless of season of sampling, while for other groups abundance near CWD was affected by timing of collection. Studies are underway to determine if large volume inputs of woody debris over an extensive area affect ground-dwelling arthropods and other organisms (McCay et al. 2002).

The higher numbers of arthropods captured near drift fences, and the higher number of taxa captured more frequently in traps along drift fences, suggest that the drift fences were more efficient at directing arthropods into pitfall traps than logs. Hansen & New (2005) found that pitfalls with barriers were more efficient at capturing beetles than those without. Our data suggest that traps near metal drift fences were more efficient than those near logs despite our efforts to prevent that. However, Sanzone (1995) also caught more arthropods overall and higher numbers of most arthropod orders in pitfalls away from logs. Sanzone's order level identifications make it difficult to compare her results to our study, but greater trap efficiency was not a factor in that study because pitfalls away from logs did not have drift fences. Our data, when considered together with Sanzone (1995), suggest that many common ground-dwelling arthropods negotiate around logs in ways that allowed them to avoid being trapped. Originally we hypothesized that many arthropods would prefer the moist, shaded habitat near logs. Again, our data and Sanzone's (1995) suggest this is not generally the case for most ground-dwelling arthropods. Although we captured 36 taxa more frequently near logs, most were known saproxylic species. For example, bark beetles (Curculionidae: Scolytinae), woodborers (Cerambycidae), and click beetle larvae (Elateridae) were captured more frequently near logs and all either directly feed in large dead wood or are commonly found in it. Likewise, Medetera spp. flies (Dolichopodidae) captured more frequently near logs are known predators of bark beetles (Goyer et al. 1980) and many crane flies (Tipulidae) and fungus gnats (Mycetophilidae) are saproxylic (McAlpine et al. 1981). Beetles in the Endomychidae, Melandryidae, and Euglenidae captured more frequently near logs are also known to be associated with fungi and rotten logs (Borror et al. 1989). Although the majority of ground beetles (Carabidae) were captured more frequently along metal drift fences, members of the genus Piemus and unidentified members of the tribe Pterostichini were captured more often near logs. Thiele (1977) points out that preferences for certain levels of humidity among the Carabidae is one of the most important factors governing their choice of habitats. Despite Thiele's (1977) conclusion that most forest carabid species preferred darkness and moist conditions, we found only a few were common near logs. In contrast, most rove beetles (Staphylinidae) were captured in greater numbers in traps near logs. Although staphylinids have diverse habits, they are frequently found near decaying material or beneath logs, stones or other debris. These results support those of Jabin et al. (2004) who also found staphylinids were more abundant close to logs. Overall, our data are similar to those of Evans et al. (2003) who found that some invertebrate groups increased in abundance in litter samples close to logs while others decreased. They speculated that logs might influence abundance of various arthropods in several ways including increased predation near logs, changes in the litter composition near logs as they decompose, and alteration of the C:N ratio of the fermentation layer near logs. Clearly arthropods are a diverse group and, as Thiele's (1977) extensive studies of carabids show, even within a single family they have highly varied habitat requirements.


Throughout the world dead wood is a critical habitat to a number of saproxylic species that are important for maintaining overall forest diversity (Elton 1966; McMinn & Crossley 1996; Braccia & Batzer 2001; Grove 2002; Grove & Hanula 2006). Experience in European forests demonstrates that without it a number of saproxylic species could go extinct or be reduced to critically low levels (Berg et al. 1994). We found that relatively few non-saproxylic macroarthropods were captured more frequently near logs even on plots where forest structure was greatly simplified by annual burning. Arthropod community structure might have been affected by the overall volume of logs on frequently burned plots but the effect was weak in comparison to the impact of fire frequency. Detailed studies are needed to clearly understand how deadwood contributes to the habitat needs of those species or groups found associated with it in longleaf pine communities.


We thank Arthur Phalo for coordinating sample collection, processing, and data entry. Numerous student workers sorted samples day in and day out, Frances Brookshire labeled sample vials and proofed data, and Lee Reynolds identified many of the specimens. We thank James Pitts for identifications of spiders and Hymenoptera, and Cecil Smith for taxonomic help on other groups. Keith Lawrence and the staff on the Osceola National Forest provided invaluable assistance throughout this study. Stan Zarnoch helped with statistical analyses, and Simon Grove and Jeff Lemieux provided helpful comments on previous drafts of this paper. Finally, we thank the Nature Conservancy's Ecosystem Research Program and the A. W. Mellon Foundation who provided funding for this work.


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SANZONE, D. M. 1995. Macroarthropods Associated with Coarse Woody Debris: Factors Influencing Abundance and Diversity Patterns. M. S. Thesis, University of Georgia, Athens.

SHEA, R. W., SHEA, B. W., KAUFFMAN, J. B., WARD, D. E., HASKINS, C. I., AND SCHOLES, M. C. 1996. Fuel biomass and combustion factors associated with fires in savanna ecosystems of South Africa and Zambia. J. Geophysical Res. 101D: 23551-23568.

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(1) USDA Forest Service, Forestry Sciences Laboratory, 320 Green St., Athens, GA 30602-2044

(2) USDA Forest Service, Department of Forest Resources, Clemson University, Clemson, SC 29634

(3) Retired


FRQ 0.84 * 0.90 * -0.08 0.93 * -0.14 0.86 *
 LIT 0.85 * -0.15 0.86 * -0.23 0.82 *
 HUM -0.13 0.96 * 0.01 0.88 *
 SWD 0.02 0.06 -0.20
 DEAD -0.12 0.90 *
 GALL 0.19


FRQ -0.54 * 0.85 * -0.36 0.15
 -0.51 * 0.76 * -0.26 0.02
 -0.60 * 0.88 * -0.45 * 0.11
 0.02 -0.18 0.15 -0.15
 -0.63 * 0.88 * -0.42 0.14
 0.19 0.04 -0.16 0.05
 -0.62 * 0.96 * -0.37 0.30
 GRASS -0.66 * 0.52 * -0.10
 LIVE -0.44 * 0.30
 DCWD -0.12

1994 TO 2000.
 Trap Location

 Drift fence
Variable (Mean [+ or -] SE) Log (Mean [+ or -] SE)

Number of arthropods 3950.1 [+ or -] 192.9 2859.1 [+ or -] 101.8
Biomass 38.9 [+ or -] 3.5 27.4 [+ or -] 2.6
 (g dry wt/plot)
Shannon Diversity (H') 3.61 [+ or -] 0.06 3.54 [+ or -] 0.06
Evenness (J) 0.67 [+ or -] 0.01 0.66 [+ or -] 0.01
Richness (genera/plot) 216.8 [+ or -] 3.46 212.1 [+ or -] 3.41

 Trap Location

Variable P > F

Number of arthropods <0.0001
Biomass (g dry wt/plot) <0.0001
Shannon Diversity (H') 0.086
Evenness (J) 0.21
Richness (genera/plot) 0.25

Analyses of variance were conducted by the SAS GLM procedure
(SAS 1985).

 Drift Fence
 Genus or
Order Family Lowest Taxa Mean SE

Scorpiones Buthidae Centruroides 11.9 1.5
Araneae Ctenizidae Ummidia 7.0 1.0
 Zoridae Zora (a) 1.8 0.6
 Gnaphosidae Callilepis 9.6 1.9
 Drassyllus 10.3 2.5
 Herpyllus (a) * 0.9 0.2
 Sergiolus 1.5 0.4
 Theridiidae Dipoena 1.7 0.6
 Linyphiidae 68.3 7.6
 Ceratinops (a) 1.8 0.4
 Erigone (a) 17.4 4.5
 Meioneta 21.9 2.6
 Tetragnathidae Pachygnatha (a) 1.3 0.3
 Thomisidae Ozyptila (a) 11.3 3.4
 Corrinidae Scotinella (a) * 0.5 0.2
 Agelenidae Cicurina (a) 3.3 1.1
 Hahnidae 48.0 9.1
 Hahnia 13.8 2.3
 Neoantistea (a) 34.2 8.5
 Lycosidae 423.8 46.5
 Allocosa (a) 1.7 0.6
 Hogna 51.5 7.2
 Immatures (a) 112.2 23.2
 Pardosa 16.2 4.1
 Pirata * 60.2 7.1
 Schizocosa 46.8 9.3
 Sosippus 19.9 2.1
 Varacosa 104.0 9.5
 Salticidae Corythalia * 4.1 0.7
 Habronattus 1.2 0.4
 Phlegra 1.0 0.3
 Sitticus (a) 2.8 0.7
Opiliones Gagrellidae Leiobunum 21.7 1.9
 Phalangiidae (a) 32.1 4.6
Isopoda (a) 7.3 1.8
Spirobolida Spirobolidae Narceus 2.5 0.4
Lithobiomorpha Lithobiidae 18.4 1.5
Orthoptera Tettigoniidae Atlanticus 1.8 0.3
 Acrididae 4.5 0.7
 Conocephalinae (a) 0.9 0.2
 Gryllacrididae Ceuthophilus 11.2 3.3
 Gryllidae 304.4 23.7
 Anaxipha (a) 6.9 1.1
 Cycloptilum 4.4 0.6
 Gryllinae (a) * 2.1 1.1
 Gryllus 21.7 1.7
 Miogryllus (a) 24.7 3.4
 Mogoplistinae (a) * 1.9 0.5
 Orocharis 2.8 0.5
 Pictonemobius 218.7 18.7
Blattaria Blattellidae Cariblatta 10.3 1.4
Isoptera Rhinotermitidae Reticulitermes 3.4 0.6
Hemiptera Reduviidae Repipta * 0.1 0.1
Homoptera * 25.0 2.5
 Cicadellidae 4.7 0.7
 Delphacidaea * 0.9 0.3
 Cixiidae * 5.3 1.0
 Oilarusa * 2.0 0.5
 Achilidae * 2.9 0.6
 Catoniaa * 0.2 0.1
 Aphididae 6.0 0.9
Coleoptera Carabidae 88.2 8.3
 Larvaea 2.4 0.6
 Anisodactylus 2.7 0.5
 Cyclotrachelus 38.0 4.6
 Megacephala a 2.3 1.1
 Pasimachus 5.9 1.8
 Piemus * 0.1 0.1
 Pterostichini * 0.2 0.1
 Pterostichus 11.8 2.6
 Scaritini 1.8 0.7
 Dytiscidae Hydaticus 1.2 0.3
 Scydmaenidae 5.6 1.7
 Staphylinidae Larvae (a) 24.9 2.3
 Aleochariinae * 0.8 0.8
 Osoriinae * 0.1 0.1
 Oxyteninae 3.5 1.1
 Steninae * 0.1 0.1
 Scarabaeidae 190.8 38.1
 Aphodius * 0.0 0.0
 Bolbocerus 4.2 0.7
 Canthon 66.6 17.4
 Onthophagus 104.9 21.6
 Trox 3.6 1.2
 Elateridae larvae * 1.6 0.3
 Lycidae Plateros * 0.3 0.1
 Cantharidae Larvaea 3.7 0.9
 Endomychidae Epipocusa * 0.0 0.0
 Melandryidae * 0.3 0.3
 Eustrophinus * 0.1 0.1
 Tenebrionidae * 26.1 2.8
 Helops 5.9 2.2
 Platydema * 1.8 0.6
 Cerambycidae * 0.6 0.2
 Prionus * 0.5 0.2
 Chrysomelidae Myochrous 2.0 0.4
 Curculionidae 20.2 2.0
 Hylobius 10.5 1.2
 Ips * 0.0 0.0
 Sphenophorus 4.1 0.6
Mecoptera Panorpidae Panorpa 6.9 1.5
Diptera Tipulidae * 0.4 0.1
 Mycetophilidae Unidentified (a) * 0.9 0.2
 Orfelia (a) 5.7 1.3
 Sciaridae 205.5 30.2
 Bradysia * 0.3 0.1
 Corynoptera (a) 198.3 30.5
 Epidapus 5.2 1.0
 Pseudosciara (*) 0.6 0.2
 Sciaraa (*) 0.1 0.1
 Culicidae 6.5 1.0
 Culex 6.1 0.9
 Chironomidae * 0.6 0.2
 Empididae 1.1 0.2
 Drapetis * 0.5 0.1
 Dolichopodidae * 11.4 1.3
 Medetera * 6.8 1.0
 Phoridae Megaselia 108.5 16.5
 Sphaeroceridae Leptocera 14.5 4.6
Lepidoptera Arctiidae larvae 9.0 1.1
 Noctuidae larvae 3.3 0.5
Hymenoptera Diapriidae 6.6 1.0
 Mutillidae 7.0 0.9
 Dasymutilla 2.7 0.5
 Timulla 3.6 0.5
 Pompilidae Priocnemella 1.8 0.3
 Formicidae 978.7 75.3
 Formica 24.1 5.0
 Leptothorax 3.8 1.3
 Monomorium 4.5 1.7
 Odontomachus 364.8 33.8
 Pheidole 353.4 35.3

 Drift Fence
Order Family Lowest Taxa Mean SE

Scorpiones Buthidae Centruroides 2.8 0.6
Araneae Ctenizidae Ummidia 3.4 0.6
 Zoridae Zora (a) 0.4 0.2
 Gnaphosidae Callilepis 4.9 1.3
 Drassyllus 7.2 1.7
 Herpyllus (a) * 5.7 1.2
 Sergiolus 0.4 0.2
 Theridiidae Dipoena 0.3 0.1
 Linyphiidae 36.7 3.4
 Ceratinops (a) 0.3 0.4
 Erigone (a) 6.0 1.3
 Meioneta 3.8 0.6
 Tetragnathidae Pachygnatha (a) 0.3 0.1
 Thomisidae Ozyptila (a) 7.5 2.9
 Corrinidae Scotinella (a) * 2.3 1.2
 Agelenidae Cicurina (a) 0.8 0.3
 Hahnidae 24.1 4.0
 Hahnia 8.0 1.9
 Neoantistea (a) 16.1 3.5
 Lycosidae 287.1 26.2
 Allocosa (a) 0.8 0.2
 Hogna 28.3 6.4
 Immatures (a) 32.0 7.9
 Pardosa 5.0 0.8
 Pirata * 86.6 7.3
 Schizocosa 36.6 7.1
 Sosippus 12.3 2.3
 Varacosa 77.8 8.2
 Salticidae Corythalia * 8.3 0.9
 Habronattus 0.2 0.1
 Phlegra 0.2 0.1
 Sitticus (a) 0.8 0.2
Opiliones Gagrellidae Leiobunum 6.6 1.5
 Phalangiidae (a) 26.9 3.4
Isopoda (a) 5.0 1.3
Spirobolida Spirobolidae Narceus 0.6 0.2
Lithobiomorpha Lithobiidae 12.9 1.2
Orthoptera Tettigoniidae Atlanticus 0.4 0.2
 Acrididae 2.5 0.5
 Conocephalinae (a) 0.3 1.0
 Gryllacrididae Ceuthophilus 3.9 1.1
 Gryllidae 126.2 11.6
 Anaxipha (a) 3.9 1.5
 Cycloptilum 0.9 0.2
 Gryllinae (a) * 14.7 1.7
 Gryllus 13.6 1.4
 Miogryllus (a) 9.3 1.4
 Mogoplistinae (a) * 3.1 0.5
 Orocharis 1.5 0.4
 Pictonemobius 77.6 9.1
Blattaria Blattellidae Cariblatta 5.6 1.1
Isoptera Rhinotermitidae Reticulitermes 1.9 0.4
Hemiptera Reduviidae Repipta * 0.4 0.1
Homoptera * 32.1 2.5
 Cicadellidae 2.8 0.6
 Delphacidaea * 2.3 0.5
 Cixiidae * 12.9 1.4
 Oilarusa * 3.6 0.8
 Achilidae * 9.2 1.5
 Catoniaa * 1.9 0.3
 Aphididae 0.3 0.1
Coleoptera Carabidae 42.6 3.3
 Larvaea 0.7 0.1
 Anisodactylus 0.6 0.1
 Cyclotrachelus 14.0 1.9
 Megacephala a 0.3 0.1
 Pasimachus 3.1 1.1
 Piemus * 1.7 0.4
 Pterostichini * 2.3 0.5
 Pterostichus 5.8 1.3
 Scaritini 0.4 0.2
 Dytiscidae Hydaticus 0.3 0.1
 Scydmaenidae 3.7 1.5
 Staphylinidae Larvae (a) 3.6 0.6
 Aleochariinae * 2.9 0.9
 Osoriinae * 2.5 0.5
 Oxyteninae 0.6 0.2
 Steninae * 4.0 0.9
 Scarabaeidae 91.1 18.9
 Aphodius * 2.4 0.8
 Bolbocerus 1.5 0.4
 Canthon 23.4 8.8
 Onthophagus 54.5 12.9
 Trox 0.7 0.2
 Elateridae larvae * 4.8 0.7
 Lycidae Plateros * 1.0 0.2
 Cantharidae Larvaea 0.2 0.2
 Endomychidae Epipocusa * 1.4 0.4
 Melandryidae * 2.2 0.6
 Eustrophinus * 1.7 0.5
 Tenebrionidae * 36.7 3.6
 Helops 0.4 0.1
 Platydema * 20.0 2.9
 Cerambycidae * 2.1 0.4
 Prionus * 1.2 0.2
 Chrysomelidae Myochrous 0.7 0.2
 Curculionidae 10.2 1.1
 Hylobius 4.8 0.7
 Ips * 2.3 0.7
 Sphenophorus 1.5 0.4
Mecoptera Panorpidae Panorpa 3.7 0.9
Diptera Tipulidae * 4.3 0.5
 Mycetophilidae Unidentified (a) * 5.2 0.8
 Orfelia (a) 0.5 0.1
 Sciaridae 86.0 19.5
 Bradysia * 2.6 1.0
 Corynoptera (a) 77.8 19.3
 Epidapus 0.6 0.3
 Pseudosciara (*) 2.2 0.6
 Sciaraa (*) 2.0 0.8
 Culicidae 1.8 0.4
 Culex 1.2 0.3
 Chironomidae * 2.8 0.9
 Empididae 3.8 1.2
 Drapetis * 1.3 0.2
 Dolichopodidae * 25.3 1.7
 Medetera * 19.8 1.8
 Phoridae Megaselia 59.6 14.5
 Sphaeroceridae Leptocera 3.7 1.0
Lepidoptera Arctiidae larvae 6.0 0.8
 Noctuidae larvae 5.6 0.7
Hymenoptera Diapriidae 1.5 0.3
 Mutillidae 2.8 0.4
 Dasymutilla 0.5 0.2
 Timulla 1.6 0.3
 Pompilidae Priocnemella 0.5 0.1
 Formicidae 764.4 37.5
 Formica 13.5 2.9
 Leptothorax 0.3 0.2
 Monomorium 0.9 0.5
 Odontomachus 339.0 33.9
 Pheidole 263.3 33.4

 Drift Fence
 Genus or
Order Family Lowest Taxa P > F

Scorpiones Buthidae Centruroides <0.0001
Araneae Ctenizidae Ummidia 0.0003
 Zoridae Zora (a) --
 Gnaphosidae Callilepis 0.004
 Drassyllus 0.04
 Herpyllus (a) * --
 Sergiolus 0.04
 Theridiidae Dipoena 0.02
 Linyphiidae <0.0001
 Ceratinops (a) --
 Erigone (a) --
 Meioneta <0.0001
 Tetragnathidae Pachygnatha (a) --
 Thomisidae Ozyptila (a) --
 Corrinidae Scotinella (a) * --
 Agelenidae Cicurina (a) --
 Hahnidae 0.0001
 Hahnia 0.01
 Neoantistea (a) --
 Lycosidae <0.0001
 Allocosa (a) --
 Hogna 0.0006
 Immatures (a) --
 Pardosa 0.02
 Pirata * 0.0005
 Schizocosa 0.05
 Sosippus 0.04
 Varacosa 0.005
 Salticidae Corythalia * 0.0002
 Habronattus 0.02
 Phlegra 0.003
 Sitticus (a) --
Opiliones Gagrellidae Leiobunum <0.0001
 Phalangiidae (a) --
Isopoda (a) --
Spirobolida Spirobolidae Narceus <0.0001
Lithobiomorpha Lithobiidae 0.001
Orthoptera Tettigoniidae Atlanticus 0.002
 Acrididae 0.02
 Conocephalinae (a) --
 Gryllacrididae Ceuthophilus 0.005
 Gryllidae <0.0001
 Anaxipha (a) --
 Cycloptilum <0.0001
 Gryllinae (a) * --
 Gryllus <0.0001
 Miogryllus (a) --
 Mogoplistinae (a) * --
 Orocharis 0.02
 Pictonemobius <0.0001
Blattaria Blattellidae Cariblatta 0.01
Isoptera Rhinotermitidae Reticulitermes 0.05
Hemiptera Reduviidae Repipta * 0.04
Homoptera * 0.04
 Cicadellidae 0.01
 Delphacidaea * --
 Cixiidae * <0.0001
 Oilarusa * --
 Achilidae * 0.0003
 Catoniaa * --
 Aphididae <0.0001
Coleoptera Carabidae <0.0001
 Larvaea --
 Anisodactylus 0.0009
 Cyclotrachelus <0.0001
 Megacephala a --
 Pasimachus 0.002
 Piemus * 0.0003
 Pterostichini * 0.0002
 Pterostichus 0.006
 Scaritini 0.02
 Dytiscidae Hydaticus 0.01
 Scydmaenidae 0.0001
 Staphylinidae Larvae (a) --
 Aleochariinae * 0.008
 Osoriinae * 0.0002
 Oxyteninae 0.007
 Steninae * 0.0005
 Scarabaeidae 0.003
 Aphodius * 0.01
 Bolbocerus 0.003
 Canthon <0.0001
 Onthophagus 0.03
 Trox 0.02
 Elateridae larvae * 0.0001
 Lycidae Plateros * 0.01
 Cantharidae Larvaea --
 Endomychidae Epipocusa * --
 Melandryidae * 0.003
 Eustrophinus * 0.003
 Tenebrionidae * 0.04
 Helops 0.01
 Platydema * <0.0001
 Cerambycidae * 0.003
 Prionus * 0.02
 Chrysomelidae Myochrous 0.006
 Curculionidae <0.0001
 Hylobius 0.0005
 Ips * 0.007
 Sphenophorus <0.0001
Mecoptera Panorpidae Panorpa 0.009
Diptera Tipulidae * <0.0001
 Mycetophilidae Unidentified (a) * --
 Orfelia (a) --
 Sciaridae <0.0001
 Bradysia * 0.04
 Corynoptera (a) --
 Epidapus <0.0001
 Pseudosciara (*) 0.01
 Sciaraa (*) --
 Culicidae 0.0003
 Culex 0.0001
 Chironomidae * 0.03
 Empididae 0.04
 Drapetis * 0.004
 Dolichopodidae * <0.0001
 Medetera * <0.0001
 Phoridae Megaselia 0.03
 Sphaeroceridae Leptocera 0.03
Lepidoptera Arctiidae larvae 0.02
 Noctuidae larvae 0.0009
Hymenoptera Diapriidae <0.0001
 Mutillidae <0.0001
 Dasymutilla 0.0003
 Timulla 0.001
 Pompilidae Priocnemella 0.0003
 Formicidae 0.02
 Formica 0.007
 Leptothorax 0.007
 Monomorium 0.04
 Odontomachus 0.05
 Pheidole 0.02

Asterisks (*) denote groups that were captured in higher
numbers near logs.

Taxa followed by an "a" had a significant interaction of
trap location with burn frequency.
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Author:Hanula, James L.; Wade, Dale D.; O'Brien, Joseph; Loeb, Susan C.
Publication:Florida Entomologist
Article Type:Report
Geographic Code:1U5FL
Date:Jun 1, 2009
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