A Method for Quantifying Foliage-Dwelling Arthropods

Environment

Your institution must subscribe to JoVE's Environment section to access this content.

Fill out the form below to receive a free trial or learn more about access:

 

Summary

We describe how to quantify leaf dwelling arthropods by sealing the leaves and end of branches in a bag, clipping and freezing the bagged material, and rinsing the previously frozen material in water to separate arthropods from the substrate for quantification.

Cite this Article

Copy Citation | Download Citations | Reprints and Permissions

Eichholz, M. W., Sierzega, K. P. A Method for Quantifying Foliage-Dwelling Arthropods. J. Vis. Exp. (152), e60110, doi:10.3791/60110 (2019).

Abstract

Terrestrial arthropods play an important role in our environment. Quantifying arthropods in a way that allows for a precise index or estimates of density requires a method with high detection probability and a known sampling area. While most described methods provide a qualitative or semi-quantitative estimate adequate for describing species presence, richness, and diversity, few provide an adequately consistent detection probability and known or consistent sampling areas to provide an index or estimate with adequate precision to detect differences in abundance across environmental, spatial, or temporal variables. We describe how to quantify leaf-dwelling arthropods by sealing the leaves and end of branches in a bag, clipping and freezing the bagged material, and rinsing the previously frozen material in water to separate arthropods from the substrate and quantify them. As we demonstrate, this method can be used at a landscape scale to quantify leaf-dwelling arthropods with adequate precision to test for and describe how spatial, temporal, environmental, and ecological variables influence arthropod richness and abundance. This method allowed us to detect differences in density, richness, and diversity of leaf-dwelling arthropods among 5 genera of trees commonly found in southeastern deciduous forests.

Introduction

Terrestrial arthropods play an important role in our ecosystem. In addition to being of scientific interest arthropods can be both detrimental and beneficial to crops, horticultural plants, and natural vegetation as well as provide an important trophic function in food webs. Thus, understanding the factors that influence arthropod community development and abundance is critical to farmers, pest control managers, plant biologists, entomologists, wildlife ecologists, and conservation biologists that study community dynamics and manage insectivorous organisms. Understanding factors that influence arthropod communities and abundances often requires the capture of individuals. Capture techniques can generally be categorized into qualitative techniques that only detect presence of a species for estimates of species range, richness, and diversity, or semi-quantitative and quantitative techniques that allow for an index or estimate of abundance and density of individuals within a taxonomic group.

Qualitative techniques that only allow inference regarding presence of a species or community structure have an unknown or intrinsically low detection probability or are lacking in providing inference regarding detection probability and size of area sampled. Because detection probability with these techniques is low, variability associated with detection precludes adequate precision for inferring how explanatory variables influence arthropod population metrics. Qualitative techniques used to estimate presence include suction sampling1, light traps2, emergence traps3, feeding patterns on roots4, brine pipes5, baits6, pheromone3, pitfall traps7, Malaise traps8, window traps9, suction traps10, beating trays11, spider webs12, leaf mines, frass13, arthropod galls14, vegetation and root damage15.

Alternatively, semi-quantitative and quantitative techniques allow researchers to estimate or at least consistently sample a specified sample area and estimate probability of detection or assume detection probability is non-directional and adequate as to not obscure the researcher's ability to detect spatial or temporal variation in abundance. Semi-quantitative and quantitative techniques include sweep nets16, suction or vacuum sampling17, systematic counting of visible arthropods18, sticky traps19, various pot-type traps20, entrance or emergent holes21, chemical knockdown22, sticky and water filled color traps23, and branch bagging and clipping24.

Recent anthropogenic-induced changes to climate and disturbance regimes have led to dramatic changes in plant communities, making interactions between plant-community species composition and arthropod communities an active area of study. Understanding how arthropod communities vary with plant species composition is a critical component for understanding the potential economic and environmental impacts of changes to plant communities. Semi-quantitative or quantitative methods of quantifying arthropod abundance with adequate precision to detect differences among species of plants are needed. In this article, we describe a method for indexing foliage-dwelling arthropods that, with reasonable effort, provided adequate precision to identify differences in individual abundance and biomass, diversity, and richness among 5 taxa of trees commonly found in the southeastern deciduous forests of North America25. This approach provided precision adequate for estimating abundance to allow inference as to how changes in species composition of forest plant communities due to anthropic modified disturbance regimes influence composition of arthropods, potentially influencing abundance and distribution of higher trophic insectivorous birds and mammals. More specifically, by using a modified bagging technique first described by Crossley et al.24, we estimated density of surface, foliage-dwelling arthropods and tested the prediction that we would detect differences in diversity, richness, and abundance of arthropods in the foliage of faster growing more xeric species of trees relative to slower growing more mesic species. The goal of this article is to provide detailed instructions of the technique.

We conducted the study on the Shawnee National Forest (SNF) in southern Illinois. The SNF is a 115,738-ha forest located in the Central Hardwoods region of the Ozarks and Shawnee Hills natural divisions26. The forest comprises a mosaic of 37% oak/hickory, 25% mixed-upland hardwoods, 16% beech/maple, and 10% bottomland hardwoods. The SNF is dominated by second growth oak/hickory in upland xeric areas and sugar maple, American beech, and tulip tree (Liriodendron tulipifera) in sheltered mesic valleys27,28.

Site selection for this method will be dependent on the overarching goals of the study. For example, the primarily goal of our original study was to provide insight into how changes in tree community might influence higher trophic organisms by comparing foliage-dwelling arthropod community metrics between mesic and xeric adapted tree communities. Thus, our primary objective was to quantify the arthropod community on individual trees located within the xeric or mesic tree community. We selected 22 study sites along an oak/hickory (xeric) to beech/maple (mesic) dominated gradient using USFS stand cover maps (allveg2008.shp) in ArcGIS 10.1.1. To prevent potential confounding effects, we selected sites using the following criteria: not located in riparian areas, ≥12 ha, and located within contiguous upland-deciduous forest habitat (i.e., elevation above 120 m). All sites contained mature trees >50 years old in hilly terrain, thus comprised similar slopes and aspects. While beech/maple site boundaries were distinguished based on the transition of tree communities, oak/hickory site boundaries were identified artificially using SNF cover maps and ArcGIS 10.1.1. All sites were large forest blocks within un-glaciated terrain; their differences in tree species composition were not due to differences in location on the landscape but were representative of past land usage (e.g., clear cuts or selective harvest). We ground-truthed the maps by uploading discrete polygon shapefiles of each study site to a handheld Global Positioning System (GPS) and verifying tree species composition. We randomly selected sampling points (n = 5) at each site. At each point, we sampled three trees from 0600−1400 hours during 23 May to 25 June 2014. To locate sample trees, we searched outward to a 30 m radius from vegetation points until mature trees (>20 cm d.b.h.) with branches low enough to sample were found. Typically, the three mature trees that represented three of the five genera (Acer, Carya, Fagus, Liriodendron, and Quercus) of interest and were closest to the center point were sampled.

Subscription Required. Please recommend JoVE to your librarian.

Protocol

1. Building the sampling device prior to going to the field

  1. Using bolt cutters, large wire cutters, or an electric grinding disk, remove the bottom 1/3 of the 30 cm wire tomato cage so that it is approximately 55 cm in length.
  2. Cut two, 50 cm braces made from aluminum or similarly semi-rigid material to use as attachment rods and braces on each side of the largest end of the tomato cage. At 38 cm from the end, use a table-top vice or large grasping tool such as channel locks to bend the brace to an approximately 30˚ angle. Attach the longer end of each of the two attachment rods to opposite sides of the tomato cage with zip ties and duct or electrical tape ensuring the tape is wrapped around at least 6 cm of the cage and rod. Be certain to wrap the tape around the cage and rod numerous times to ensure the cage is permanently attached to the rod.
  3. Attach the other end of each of the two attachment rods on the opposite sides of the end of an extendable pole with zip ties and duct or electrical tape. As before, wrap the tape multiple times to affix it permanently ensuring the tape overlaps the pole and rods by at least 6 cm. Be certain the opening of the cage is in contact with the end of the telescoping pole when the cage is attached.
  4. Attach the cage directly onto the end of the pole using zip ties and electrical or duct tape. Attach hook-and-loop fastener strips at 3 points to the opening of the cage 90˚ from the previously attached pole.
    NOTE: These strips will be use later to keep the bag open.

2. Enclosing the branch

  1. Attach 2 pieces of hook-and-loop fastener to the outside opening of the bag so they align with the hook-and-loop fastener attached to the opening of the cage. These will be used to hold the opening of the bag in place while it is brought over a sample branch. Be certain the hook-and-loop fastener is aligned so when the bag is inserted and attached, the opening to the pull strings of the bag run parallel to the telescoping pole.
  2. Insert a ~49 L kitchen garbage bag in the wire tomato cage. Place one gator clip on each respective side of the bottom of the bag and attach the clips to both the bag and wire cage to hold the bag against the cage. Repeat the same procedure for the top of the bag with one gator clip attached to the pull string and the wire cage opposite of the pole. 
  3. Attach para cord to  the bag’s draw string closest to the pole. Cut four pieces of plastic or hard rubber tubing in 4 cm sections and attach with duct or electrical tape at four locations. The first should be placed on the extending part of the pole about 0.5 m from the end of the pole nearest the tomato cage.  The remaining 3 should be placed equidistance along the bottom section of the extending pole starting about 5 cm from the top of the bottom section (i.e., one each along the top, middle and bottom). Thread the end of the para cord that is not attached to the bag through the plastic tubing.
  4. For each sample tree, use a random number generator to select a sample height that is within the height of the extension pole when extended at maximum length. Use a random number generator to select a sample distance from the tree trunk. Identify a branch that will fit in the bag with minimal disturbance to the foliage and is the height and distance from the trunk based on the numbers generated from the random number generator.
  5. Raise the sampling pole to a height parallel with the desired branch. Quickly slide the bag over the branch then rapidly pull the para cord strings attached to the draw strings on the bag to seal the bag. Practice this a few times prior to the first attempt to become efficient at incorporating the foliage with minimal disturbance to the leaves.
  6. Have a second person clip the branch at the location adjacent to the bag's opening with the extension pole pruner. Carefully bring the sample bag to the ground and rapidly tie the bag's draw strings closed. Attempt to complete the bagging, cutting, and bag-tying steps as quickly as possible to prevent insects from escaping.
  7. Store the bagged branch in a freezer until ready to conduct the laboratory arthropod analysis.

3. Arthropod analysis

  1. Hold the frozen bag and branch upright and shake the sample branch while in the bag to dislodge arthropods into the bag. Carefully remove the branch and rinse in large collection pan to remove remaining arthropods. Empty remaining material from the bag into the collection pan. Remove any non-arthropod debris.
  2. Separate arthropods into desired taxonomic groups. Note differences between larvae and adults.
  3. Quantify arthropods as desired. If biomass is of interest, either measure length of arthropods and use published length mass table to estimate biomass, or place arthropods in small drying pans, dry in drying oven for 24 h at 45 °C, and weigh on an electronic balance.

4. Estimating density

  1. To estimate density and control for variation in leaf structure and leaf density between samples within tree species and among tree species either:
  2. Count and measure the surface area of the leaves from each sample.
  3. Dry the leaves in a drying oven for 48 h at 45 °C and weigh the leaves on an electronic balance.
  4. Measure the length of all woody branch within the sample.
    NOTE: Diel differences occur in arthropod communities, so sampling should be conducted throughout the entire period of inference.

Subscription Required. Please recommend JoVE to your librarian.

Representative Results

We collected 626 samples from 323 individual trees composing 5 tree groups. For estimates of total arthropod biomass per meter of branch sampled, the standard error ranged from 12% to 18% of the mean for the 5 tree groups (Table 1). This level of precision was adequate to detect variation among tree groups and a quadratic change in biomass with date25. This technique provided more precision when estimating guild diversity as demonstrated by the standard error of arthropod guild diversity (H') ranging from 3% to 7% of the mean diversity across the 5 tree groups (Table 1). Precision at this level was adequate to detect variation across the 5 tree groups25. Precision of estimates of richness was also very good as demonstrated by standard errors that ranged from 3% to 7% of the mean richness among the 5 tree groups (Table 1). This level of precision was adequate to identify variation among tree groups, a quadratic association with date, decrease in richness with height on the tree, and a positive relationship between arthropod richness and distance from the tree trunk25.

Tree species Richness Biomass Shannon Diversity
X SE % of mean X SE % of mean X SE % of mean
Maple spp. (N = 140) 3.54 0.17 5% 0.003 0.0004 13% 0.86 0.05 6%
Hickory spp. (N = 141) 4.62 0.20 4% 0.013 0.002 15% 1.10 0.04 4%
Tulip Poplar (N = 70) 4.32 0.20 5% 0.011 0.002 18% 1.12 0.05 4%
American Beach (N = 67) 3.23 0.22 7% 0.002 0.0003 15% 0.81 0.06 7%
Oak spp. (N = 208) 4.77 0.15 3% 0.006 0.0007 12% 1.10 0.03 3%

Table 1: Parameter estimates from most parsimonious model25. The mean (X), standard error of the mean (SE), and percentage of the mean of the standard error for each community metric of foliage dwelling-arthropods captured on 5 groups of trees using the described branch clipping method in the Shawnee National Forest in southern Illinois.

Subscription Required. Please recommend JoVE to your librarian.

Discussion

Two necessities of accurately quantifying arthropod communities are relatively high detection probabilities and known or consistent sampling areas. When sampling for arthropods, less than 100% detection probability can be attributed to either individual arthropods avoiding traps or some individuals that were trapped being undetected during processing. Interceptor traps that intercept flying arthropods (Malaise/window traps, sticky traps, etc.) appear to be the most frequently used approach to enumerate arthropod communities in the forest canopy29,30,31. These types of traps can be placed throughout the canopy, are effective at intercepting flying arthropods, and typically preserve arthropods for long periods (weeks or months) for later identification and quantification29,30,31, though they are typically limited in their ability to trap crawling arthropods31. Interceptor traps that attract arthropods using light or pheromones have additional limitations in that they trap only night flyers and their attractiveness varies with taxon, moonlight, background illumination, and cloud cover impact32,33. Additionally, because arthropods captured in interceptor traps are from unknown distances, the area trapped is unknown. As such, although interceptor traps are effective for indexing flying arthropods across an environmental gradient, data produced from interceptor traps cannot be used to estimate arthropod density25.

An additional method frequently used to monitor foliage arthropods is chemical knockdown34,35. Chemical knockdown can be very effective for collecting diverse groups of arthropods providing accurate estimates of taxonomic richness and diversity. However, this method is expensive and time consuming, is non-specific as it samples all arthropods on the tree including those on the bark and branches, may have unintended environmental impacts due to wind drift, and is illegal in some areas36,37,38,39.

Branch bagging has been demonstrated as an effective method to estimate arthropod density from surface tree foliage with an adequately high capture probability to detect variation across various environmental gradients24,40. The wire tomato cages and 49 L garbage bags used in this study allowed researchers to encompass the branch fully with little to no disturbance prior to the closure of the opening of the bag. As such, it is important that researchers are careful to not disturb foliage of the desired branch sample prior to enclosing it with the sampling bag. Thus, a critical step is to bring the sampling bag parallel with the desired sampling bag and swiftly enclose, seal, and tie the bag after each sample is collected. Sample collection is limited to the maximum height the researcher can hold an extended telescoping pole at (8 m in our study), although the same branch-bagging equipment and methodology can be used in other situations such as suspension in the canopy. Some authors have suggested that when using this procedure active, flighted-arthropods are underrepresented40,41,42. However, we believe that as long as the foliage remains undisturbed until it is enclosed by the sampling bag, it is unlikely that a substantial number arthropods present in or on the foliage at the time escaped capture. The results of our study support this assertion in that when sampling a reasonable number of trees (323), the standard error was at most 17% of the relative arthropod biomass mean (Cayra = 11%, Acer = 12%, Fagus = 17%, Liriodendrum = 15%, and Quercus = 11%). Similarly, when considering guild richness and diversity, the most variable estimate was diversity on Fagus, with a standard error that was 7% of the mean. Clearly these estimates provided adequate precision to model differences among tree genera groups as well as other ecological or environmental variables. A limitation to our results, however, is that although we are confident that detection probability with this method is high, i.e., likely nearing 100%, we do not have a method of independently verifying this assertion. Thus, while we demonstrated detection probability is adequate to detect variation across an environmental variable, which in this case was tree genera, biomass estimates produced from this methodology have the potential to be biased low by some unknown amount40.

Most authors have examined the content of the bag in the field36,42,43,44,45. We believe a critical step to maximizing detection is to freeze the bag as we did, then examine and quantify the content in the laboratory under controlled conditions. We believe this approach will decrease measurement error by minimizing the number of trapped arthropods that are overlooked or are misidentified.

Estimating the area sampled for comparison of density among tree species may be problematic if leaf structure varies considerably among tree species, as was the case in our study. In past studies, when authors were interested in quantifying foliage-dwelling arthropods, they often estimated sampling area by weighing leaves to estimate the amount of substrate available for arthropods46,47,48. The various species of oak trees, however, tend to have thicker waxy leaf cuticles than other tree species. Thus, the mass to surface area ratio for oaks is greater than other species49. Because the mass to surface area ratio is greater in oaks, using the mass of leaves as an estimate of substrate for foliage dwelling arthropods would overestimate sampling area and underestimate arthropod density for oak trees relative to tree species with less thick leaf cuticles. Additionally, if the ability to support arthropods varies among tree species, the surface area of the landscape covered by a given tree species will dictate the level of substrate supported within a specified landscape. Because the amount of surface area a given tree occupies is determined by crown spread (i.e., branch spread outwards from the trunk) and leaf density varies among trees, we believe when quantifying arthropods for consumption by insectivores, total branch length sampled is more appropriate than leaf biomass when estimating total area sampled. Our results again appear to support this assertion in that we detected differences among tree groups consistent with the predicted pattern based on previous studies25. We believe arthropod abundance or biomass per measure of branch length is most appropriate when the primary objective is to compare resources provided for insectivores among tree species. If, however, individuals are comparing tree species that produce leaves with similar leaf cuticle thickness, using leaf biomass as an estimate of sampling area may be more appropriate. Regardless of whether researchers use actual leaf area, leaf area as estimated by leaf biomass, or total branch length as a quantifiable metric, by using the bagging technique, a measurable quantity of arthropods at a specific point in time on a measurable surface area is captured per sample. This allows researchers to use leaf surface area, leaf area as estimated by leaf biomass, or total branch length as a quantifiable metric. This method provides a consistent estimate for comparing quantified arthropods among spatial or temporal variables and an estimate of arthropod density25.

In general, the sampling method described in this article appears to be effective at allowing for spatial or temporal comparisons of foliage-dwelling arthropod metrics. This approach is affordable and feasible at the landscape scale. Furthermore, although freezing the entire branch requires substantial freezer space, freezing the branch then rinsing the branch in water is an effective way to separate arthropods from foliage with minimal effort, therefore providing a cost-efficient approach to obtaining arthropod metrics. Finally, because the primary objective of our original study was to better understand how mesophication of southeastern deciduous forests is likely to impact forest-dwelling insectivorous birds and mammals we grouped arthropods into guilds based on diagnostic morphological features. However, we do not see a reason why these capture techniques cannot be used to quantify arthropods at the species or any other taxonomic level.

Subscription Required. Please recommend JoVE to your librarian.

Disclosures

The authors have nothing to disclose.

Acknowledgments

The authors would like to thank the U.S. Department of Agriculture Forest Service for funding this project through USFS Agreement 13-CS-11090800-022. We would like to thank J. Suda, W. Holland, and others for laboratory assistance, and R. Richards for field assistance.

Materials

Name Company Catalog Number Comments
13 gallon garbage bags Glad 78374
Aluminum rod Grainger 48ku20
Pruner Bartlet arborist supply pp-125b-2stick
Telescoping pole BES TPF620
Tomato Cage Gilbert and Bennet 42 inch galvanized

DOWNLOAD MATERIALS LIST

References

  1. Arnold, A. J. Insect sampling without nets, bags, or filters. Crop Protection. 13, 73-76 (1994).
  2. Roberts, R. J., Campbell, A. J., Porter, M. R., Sawtell, N. L. Funturations in the abundance of pasture scarbs in relation to Eucalyptus trees. Proceeding of the 3rd Australian Conference on Grassland Invertebrate Ecology. Lee, K. E. SA Government Printer. Adelaide. 75-79 (1982).
  3. Southwood, T. R. E., Henderson, P. A. Ecological Methods. Blackwell Science. Oxford. (2000).
  4. Masters, G. J. Insect herbivory above- and belowground: individual and joint effect on plant fitness. Ecology. 79, 1281-1293 (1995).
  5. Stewart, R. M., Kozicki, K. R. DIY assessment of leatherjacket numbers in grassland. Proceedings of the crop protection in North Britain Conference. Scottish Crop Research Institute. Dundee. 349-353 (1987).
  6. Ward, R. H., Keaster, A. J. Wireworm baiting: use of solar energy to enhance early detection of Melanotus depressus, M. verberans, and M. mellillus in Midwest cornfields. Journal of Economic Entomology. 70, 403-406 (1977).
  7. Barber, H. S. Traps for cave inhabiting insects. Journal of the Elisha Michell Scientific Society. 46, 259-266 (1931).
  8. Malaise, R. A new insect trap. Entomologisk Tidskrift. 58, 148-160 (1937).
  9. Peck, S. B., Davis, A. E. Collecting small beetles with large-area "window traps". Coleopterists Bulletin. 34, 237-239 (1980).
  10. Taylor, L. R. An improved suction trap for insects. Annals of Applied Biology. 38, 582-591 (1951).
  11. White, T. C. R. A quantitative method of beating for sampling larvae of Selidosema suavis (Lepidoptera: Geometridae) in plantations in New Zealand. Canadian Entomologist. 107, 403-412 (1975).
  12. Ozanne, C. M. Techniques and methods for sampling canopy insects. Insect Sampling in Forest Ecosystems. Leather, S. R. Blackwell Publishing. Malden, MA. 146-167 (2005).
  13. Sterling, P. H., Hambler, C. Coppicing for conservation: do hazel communities benefit?. Woodland conservation and research in the Clay Veil of Oxfordshire and Buckinghamshire. Kirby, K., Wright, F. J. NCC. Peterborough. 69-80 (1988).
  14. Fidgen, J. G., Teerling, C. R., McKinnon, M. L. Intra- and inter-crown distribution of eastern spruce gall adelgid, Adelges abietis (L.), on young white spruce. Canadian Entomologist. 126, 1105-1110 (1994).
  15. Prueitt, S. C., Ross, D. W. Effect of environment and host genetics on Eucosma sonomana (Lepidopter; Tortricidae) infestation levels. Environmental Entomology. 27, 1469-1472 (1998).
  16. Gray, H., Treloar, A. On the enumeration of insect populations by the method of net collection. Ecology. 14, 356-367 (1933).
  17. Dietrick, E. J. An improved backpack motor fan for suction sampling of insect populations. Journal of Economic Entomology. 54, 394-395 (1961).
  18. Speight, M. R. Reproductive capacity of the horse chestnut scale insect, Pulvinaria regalis Canard (Hom., Coccidae). Journal of Applied Entomology. 118, 59-67 (1994).
  19. Webb, R. E., White, G. B., Thorpe, K. W. Response of gypsy moth (Lepidoptera: Lymantriidae) larvae to sticky barrier bands on simulated trees. Proceeding of the Entomological Society of Washington. 97, 695-700 (1995).
  20. Agassiz, D., Gradwell, G. A trap for wingless moths. Proceedings and Transactions of the British Entomological and Natural History Society. 10, 69-70 (1977).
  21. Lozano, C., Kidd, N. A. C., Jervis, M. A., Campos, M. Effects of parasitoid spatial hererogeneity, sex ratio and mutual interaction between the olive bark beetle Phloeotribus scarabaeoides (Col. Scolytidae) and the pteromalid parasitoid Cheiropachus quardrum (Hum. Pteromalidae). Journal of Applied Entomology. 121, 521-528 (1997).
  22. Roberts, H. R. Arboreal Orthoptera in the rain forest of Costa Rica collected with insecticide: a report on grasshoppers (Acrididae) including new species. Proceedings of the Academy of Natural Sciences, Philadelphia. 125, 46-66 (1973).
  23. Disney, R. H. L., et al. Collecting methods and the adequacy of attempted fauna surveys, with reference to the Diptera. Field Studies. 5, 607-621 (1982).
  24. Crossley, D. A. Jr, Callahan, J. T., Gist, C. S., Maudsley, J. R., Waide, J. B. Compartmentalization of arthropod communities in forest canopies at Coweeta. Journal of the Georgia Entomological Society. 11, 44-49 (1976).
  25. Sierzega, K. P., Eichholz, M. W. Understanding the potential biological impacts of modifying disturbance regimes in deciduous forests. Oecologia. 189, 267-277 (2019).
  26. Schwegman, J. The natural divisions of Illinois. Guide to the vascular flora of Illinois. Mohlenbrock, R. H. Southern Illinois University Press. Carbondale, IL. 1-47 (1975).
  27. Fralish, J. S., McArdle, T. G. Forest dynamics across three century-length disturbance regimes in the Illinois Ozark hills. American Midland Naturalist. 162, 418-449 (2009).
  28. Thompson, F. R. The Hoosier-Shawnee Ecological Assessment. General Technical Report. NC-244. 3rd Edition, U.S. Department of Agriculture, Forest Service, North Central Research Station. St. Paul, MN. (2004).
  29. Townes, H. A light-weight Malaise trap. Entomological News. 83, 239-247 (1972).
  30. Wilkening, J., Foltz, J. L., Atkonson, T. H., Connor, M. D. An omnidirectional flight trap for ascending and descending insects. Canadian Entomologist. 113, 453-455 (1981).
  31. Basset, Y. A composite interception trap for sampling arthropods in tree canopies. Australian Journal of Entomology. 27, 213-219 (1988).
  32. Bowden, J. An analysis of factors affecting catches of insects in light traps. Bulletin of Entomological Research. 72, 535-556 (1982).
  33. Müller, J., et al. Airborne LiDAR reveals context dependence in the effects of canopy architecture on arthropod diversity. Forest Ecology and Management. 312, 129-137 (2014).
  34. Mound, L. A., Waloff, N. The components of diversity. Diversity of Insect Faunas. Symposia of the Royal Entomological Society of London No. 9. 19-40 (1978).
  35. Southwood, T. R. E., Moran, V. C., Kennedy, C. E. J. The assessment of arboreal insect fauna-comparisons of knockdown sampling and faunal lists. Ecological Entomology. 7, 331-340 (1982).
  36. Majer, J. D., Recher, H. F. Invertebrate communities on Western Australian eucalypts: a comparison of branch clipping and chemical knockdown. Australian Journal of Ecology. 13, 269-278 (1988).
  37. Basset, Y. The arboreal fauna of the rainforest tree Argyrodendron actinophyllum as sampled with restricted canopy fogging: composition of the fauna. Entomologist. 109, 173-183 (1990).
  38. Majer, J. D., Recher, H., Keals, N. Branchlet shaking: a method for sampling tree canopy arthropods under windy conditions. Australian Journal of Ecology. 21, 229-234 (1996).
  39. Moir, M. L., Brennan, K. E. C., Majer, J. D., Fletcher, M. J., Koch, J. M. Toward an optimal sampling protocol for Hemiptera on understorey plants. Journal of Insect Conservation. 9, 3-20 (2005).
  40. Johnson, M. D. Evaluation of arthropod sampling technique for measuring food availability for forest insectivorous birds. Journal of Field Ornithology. 71, 88-109 (2000).
  41. Cooper, R. J., Whitmore, R. C. Arthropod sampling methods in ornithology. Studies in Avian Biology. 13, 29-37 (1990).
  42. Cooper, N. W., Thomas, M. A., Garfinkel, M. B., Schneider, K. L., Marra, P. P. Comparing the precision, accuracy, and efficiency of branch clipping and sweep netting for sampling arthropods in two Jamaican forest types. Journal of Field Ornithology. 83, 381-390 (2012).
  43. Schowalter, T. D., Webb, J. W., Crossley, D. A. Jr Community structure and nutrient content of canopy arthropod in clearcut and uncut forest ecosystems. Ecology. 62, 1010-1019 (1981).
  44. Majer, J. D., Recher, H. F., Perriman, W. S., Achuthan, N. Spatial variation of invertebrate abundance within the canopies of two Australian eucalypt forests. Studies in Avian Biology. 13, 65-72 (1990).
  45. Beltran, W., Wunderle, J. M. Jr Temporal dynamics of arthropods on six tree species in dry woodlands on the Caribbean Island of Puerto Rico. Journal of Insect Science. 14, 1-14 (2014).
  46. Schowalter, T. D., Crossley, D. A. Jr, Hargrove, W. Herbivory in forest ecosystems. Annual Review of Entomology. 31, 177-196 (1986).
  47. Summerville, K. S., Crist, T. O. Effects of timber harvest on Lepidoptera: community, guild, and species responses. Ecological Applications. 12, 820-835 (2002).
  48. Barbosa, P., et al. Associational resistance and associational susceptibility: having right or wrong neighbors. Annual Review of Ecology, Evolution, and Systematics. 40, 1-20 (2009).
  49. Burns, R. M., Honkala, B. H. Silvics of North America: Vol 2. Hardwoods. Agriculture Handbook 654. U.S. Department of Agriculture Forest Service. Washington, D.C. (1990).

Comments

0 Comments


    Post a Question / Comment / Request

    You must be signed in to post a comment. Please or create an account.

    Usage Statistics