Method Article

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

DOI:

10.3791/56150

āø±

November 1st, 2017

Ā ,Ā 
1USDA-ARS, Arid-Land Agricultural Research Center, 2Department of Entomology, Maricopa Agricultural Center, University of Arizona

In This Article

Summary

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

Life tables allow quantification of the sources and rates of mortality in insect populations and contribute to understanding, predicting and manipulating population dynamics in agroecosystems. Methods for conducting and analyzing cohort-based life tables in the field for an insect with sessile immature life stages are presented.

Abstract

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

Life tables provide a means of measuring the schedules of birth and death from populations over time. They also can be used to quantify the sources and rates of mortality in populations, which has a variety of applications in ecology, including agricultural ecosystems. Horizontal, or cohort-based, life tables provide for the most direct and accurate method of quantifying vital population rates because they follow a group of individuals in a population from birth to death. Here, protocols are presented for conducting and analyzing cohort-based life tables in the field that takes advantage of the sessile nature of the immature life stages of a global insect pest, Bemisia tabaci. Individual insects are located on the underside of cotton leaves and are marked by drawing a small circle around the insect with a non-toxic pen. This insect can then be observed repeatedly over time with the aid of hand lenses to measure development from one stage to the next and to identify stage-specific causes of death associated with natural and introduced mortality forces. Analyses explain how to correctly measure multiple mortality forces that act contemporaneously within each stage and how to use such data to provide meaningful population dynamic metrics. The method does not directly account for adult survival and reproduction, which limits inference to dynamics of immature stages. An example is presented that focused on measuring the impact of bottom-up (plant quality) and top-down (natural enemies) effects on the mortality dynamics of B. tabaci in the cotton system.

Introduction

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

Life tables are a common tool with a long history in ecology1,2. Life tables are essentially a schedule of the births and deaths in a population over time and such data can be used to quantify a number of parameters important to understanding and predicting population dynamics. Life tables may also provide information on causes of death that are important to understanding trophic interactions and in developing control strategies for managing pests in agricultural and natural systems. Numerous field-based life tables have been constructed for insects3,4,5, and analyses have provided important insights into the dynamics, regulation and prediction of insect populations in many managed and natural systems6,7,8,9,10,11,12,13,14. The term life table is also often used to describe laboratory based studies that largely examine schedules of births and deaths but under artificial conditions that do not expose the insect to natural mortality forces and realistic environmental variables. Generally, the goal of laboratory studies is to estimate the comparative biotic potential of a species. The focus of the methods described here is for field based investigations that define realized potential relative to the environment.

Life tables can be characterized as horizontal, in which a real cohort of equal aged individuals are followed from the beginning of their lives until death, or vertical, where frequent samples are taken through time of a population with an assumed stable age structure and then vital rates are inferred from mathematically constructed cohorts2,15. The type of life table that can be deployed depends on the nature of the insect. Horizontal life tables can often be developed for univoltine (one generation per year) insects, while such an approach can be very challenging for a multivoltine insect with multiple and widely overlapping generations each year. A host of analytical methods have been proposed and used to develop vertical life tables for insect populations (see Southwood2 for examples). The methodology demonstrated here allows for the development of cohort-based, horizontal life tables in the field for multivoltine insects with specific life history characteristics, notably, the presence of sessile life stages. The method is demonstrated for a key pest in cotton as a model system.

The whitefly, Bemisia tabaci biotype B (= Bemisia argentifolii, Middle East-Asia Minor 116) is a global pest of agriculture that negatively impacts yield and quality in many agronomic and horticultural crops, including protected agricultural systems in temperate regions17. Impacts occur due to phloem feeding that disrupts nutrient flow, disorders of unknown etiology caused by nymphal feeding, transmission of numerous plant viruses and crop quality effects due to the deposition of honeydew18,19. The insect has a broad host range and is multivoltine, having as many as 12-13 generations per year depending on region and available food resources20. Management challenges also are exacerbated by its high reproductive potential, its ability to disperse and migrate within and between agricultural systems, its lack of a quiescent stage (diapause or estivation) and its disposition to rapidly develop resistance to insecticides used for suppression21,22.

Considerable progress has been made in developing integrated pest management (IPM) strategies to effectively and economically manage populations of this pest in affected crops23,24,25. These management systems were predicated on a sound fundamental understanding of the population dynamics of B. tabaci and life tables have been a key technique that have enabled this understanding. In Arizona, life tables have allowed the estimation and identification of important mortality forces for B. tabaci in multiple crop systems13,26, have enabled the measurement of mortality dynamics relative to management strategies including non-target effects of insecticides14, have provided a means of estimating potential functional non-target effects of transgenic cotton producing insecticidal proteins27, have supported rigorous assessment of a classical biological control program28 (Naranjo, unpublished data) and helped to explore the comparative effects of top-down and bottom-up effects on pest dynamics29. All of these applications have deployed the methodology described here. The approach could be useful for the study of insect population ecology in a number of natural and managed systems.

Access restricted. Please log in or start a trial to view this content.

Protocol

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

NOTE: The techniques described below are considered partial life tables because they do not explicitly include reproduction or mortality of the adult stages. The term cohort is equivalent to generation because it examines mortality from the egg to the adult stage.

1. Establish Field Sites

  1. Conduct life tables at any time during the growth of the crop once insects are present. The choice of when to initiate studies will depend on the goals and objectives of the research.
  2. Select two rows of crop near the center of the plot to minimize edge effects from surrounding plots or uncultivated areas. Mark the head of each row with a wire flag or a wooden stake to facilitate easy relocation once the crop gets large.
  3. Move 3-4 m in from edge of plot parallel to the row. Use one row to establish egg cohorts and the second to establish nymph cohorts

2. Establish Egg Cohorts

  1. Use an 8X hand lens to search for newly laid eggs on the underside of leaves near the top of the main stem of the cotton plant (generally the second or third node from the top).
    NOTE: Life stages of B. tabaci are generally distributed vertically in the plant canopy with eggs near the top of the plant and progressively older nymphal stages below. This results because 1st instar nymphs settle on the same leaf as the egg and the plant adds leaves above the initial oviposition site as it grows.
    1. Use a higher power 15X lens to observe fresh eggs and verify identification before using the 8x lens to mark the insect. Fresh eggs have a bright white coloration under the lens and stand out from eggs that are older (Figures 1A and 1B). Eggs darken to a tannish color as they mature.
  2. Use a non-toxic, ultra-fine-point black permanent marker to draw a small circle around the egg. Draw the circle small enough to minimize the chance of a female laying another egg within the circle later on.
    1. Cut a hole or slot in the side of the 8X hand lens with a hacksaw or drill bit so that the pen can be inserted and viewed through the lens when drawing (Figure 2A).
  3. Repeat this process on the same leaf, if possible, to mark other eggs, marking a total of no more than four eggs on a single leaf and no more than one egg per leaf sector. For cotton, the leaf is subdivided into four sectors by three major leaf veins (Figure 3).
  4. Tie a small lightweight cardboard tag around the petiole of the leaf containing marked egg(s). Number the tag and include notation for the plot or treatment number depending on the experimental design (Figure 2B).
  5. Tie a 1 m long length of flagging tape around the main stem near the top of the plant. Use a bow-style tie so that the tape can be easily relocated as necessary to keep it visible for repeated visits to this location in the field.
  6. Record leaf number and positional information on a portable analog or electronic notebook (Table 1). Positional information uses the sectors of the leaf to more finely note location (e.g., 1-1, 1-2, 1-4 denote eggs in sectors 1, 2 and 4 on leaf #1).
  7. Establish each cohort on a single day.
    NOTE: A cohort in a given plot is comprised of a minimum of 50 eggs total.
    1. Use no more than one leaf per plant to better distribute the members of the cohort.
      NOTE: Depending on insect density, this may comprise anywhere from 13 leaves with 3-4 eggs each or 50 leaves with one egg each. The individual plants are selected to distribute the marked leaves along as much of the row as possible.

3. Establish Nymph Cohorts

  1. Use an 8X hand lens to search for newly settled 1st instar nymphs on the underside of leaves about 3-5 leaves down from the top of the main stem of the cotton plant. Use a 15X lens to verify identification before marking using the 8X lens.
  2. Use a non-toxic, ultra-fine-point black permanent marker to draw a small circle around the nymph. Draw the circle small enough to minimize the chance of a crawler settling within the circle later on.
    NOTE: Newly hatched 1st instar nymphs are called crawlers, which can move several cm during the first few hours after egg hatch. It then "settles" onto a site where it will feed and molt without ever moving again until the adult emerges. These settled 1st instar nymphs (Figure 1C) are distinctive from the crawlers. First, they are immobile and second, they are more 2-dimensional and lay tight and flat on the leaf and have a slightly more translucent amber color.
    1. Cut a hole or slot in the side of the 8X hand lens with a hacksaw or drill bit so that the pen can be inserted and viewed through the lens when drawing (Figure 2A).
  3. Repeat this process on the same leaf, if possible, to mark other nymphs, marking no more than four nymphs on a single leaf and no more than one nymph per leaf sector. For cotton, the leaf is subdivided into four sectors by three major leaf veins (Figure 3).
  4. Tie a small lightweight cardboard tag around the petiole of the leaf containing marked nymph. Number the tag and include notation for the plot or treatment number depending on the experimental design (Figure 2B).
  5. Tie a 1 m long length of flagging tape around the main stem near the top of the plant. Use a bow-style tie so that the tape can be easily relocated as necessary to keep it visible for repeated visits to this location in the field.
  6. Record leaf number and positional information on a portable analog or electronic notebook (Table 1). Positional information uses the sectors of the leaf to more finely note location (e.g., 1-1, 1-2, 1-4 denote nymphs in sectors 1, 2 and 4 on leaf #1).
  7. To ensure that settled 1st instar nymphs and not crawlers are marked, go back to each marked leaf and observe the marked insect about 1-2 h after the initial set-up. Remarking of settled nymphs may be necessary.
  8. Establish each cohort on a single day.
    NOTE: A cohort in a given plot is comprised of a minimum of 50 nymphs total. No more than one leaf is used per plant to better distribute the members of the cohort. Depending on insect density, this may comprise anywhere from 13 leaves with 3-4 nymphs each or 50 leaves with one nymph each. The individual plants are selected to distribute the marked leaves along as much of the row as possible. Because of the 1st instar crawler stage, the eggs marked in protocol 2 are not the same insects that are then followed as nymphs. Thus, no crawler mortality is measured and the life table is slightly disjointed in time because egg and nymph cohorts are typically established on the same day. Research has shown the crawler mortality is negligible and can be essentially ignored30.

4. Observation and Recording of Egg Hatch and Mortality

  1. After 8-10 d (28-32 Ā°C; average Arizona summer conditions) after establishment of egg cohorts, collect leaves containing marked eggs and return to the laboratory for observation under a dissecting microscope. Eggs are too small to clearly evaluate mortality and causes of mortality in the field.
  2. Determine causes of death for eggs and record in the notebook initiated at cohort establishment (Table 1).
    NOTE: Death is characterized as dislodgement, predation or inviability. Dislodgement: the egg is missing due to weather events (wind, blowing dust, rain) or chewing predation. Predation: sucking predators leave behind a collapsed chorion (Figure 4K). Hatched eggs can appear collapsed, but there will be a vertical slit in the egg chorion. Use a minuten pin to tease the chorion on the leaf under the microscope to look for this slit. Inviability: eggs fail to hatch after the 8-10 d period and are a dark tan color. Under Arizona summer conditions (28-32 Ā°C) eggs would normally hatch in 5-7 days. This may differ in other regions and adjustments may be necessary in collection time from the field.

5. Observation and Recording of Nymphal Development and Mortality

  1. One to two days after cohort establishment, use a 15X lens to assess the development of nymphs and to assign a cause of mortality if dead. Make observations at least three times per week (every other day).
    1. Use relative size (Figure 1C-G) and time after establishment to assess instar.
      NOTE: There are four nymphal instars and development is rapid under Arizona summer conditions (28-32 Ā°C) with each of the first three stages lasting around 2 d and the final stage lasting 3-5 d (total nymphal development 2 wk or less). New observers should learn instar sizes by observing insects reared in the laboratory or greenhouse on the host plant of interest. The relative volume in the abdomen of the bacteriosomes (symbiont harboring organs of the whitefly) relative to overall body size is a helpful indicator of nymphal instar (Figure 1C-G). Newly molted nymphs are very flat and translucent. Nymphs ready to molt are more turgid, domed in profile, and opaque in appearance.
    2. Determine causes of death for nymphs and record in the notebook initiated at cohort establishment (Table 1).
      NOTE: Death is characterized as dislodgement, parasitism, predation or unknown depending on instar (Figure 4). Dislodgement: nymphs of any stage are missing due to weather events (wind, blowing dust, rain) or chewing predation. Estimate the stage of dislodged nymphs as the average stage of dead and live nymphs on a given observation date. Parasitism: only observable in 4th instar nymphs. The paired yellowish bacterisomes are displaced by the developing parasitoid larva (Figure 4A); the larval stage is sometimes visible (Figure 4C). The pupal stage of the parasitoid is distinctive and genera specific (Figure 4B, 4D). Predation: sucking predators will evacuate the contents of the nymph and leave behind a collapsed cadaver (Figure 4G-4I). Rarely, a chewing predator may leave evidence (Figure 4J). Unknown: death that cannot be attributed to one of the above causes. In humid environments, fungal disease may be an additional cause of death. This category might also include nymphs killed by parasitoid host-feeding. Nymphs that survive emerge as adults leaving a distinctive t-shaped slot in the exuviae (Figure 1H)
  2. Record development stage (if alive) and cause of death and stage in the notebook initiated at cohort establishment (Table 1).
  3. Once all the nymphs being observed on a single leaf have either died or emerged as an adult whitefly, collect the leaf and return to the laboratory. Use the higher magnification of a dissecting microscope to confirm that the cause of death noted in the field is accurate and make any corrections.
    NOTE: Not every non-dislodged dead insect will remain on the leaf over the typical two-week observation period and so some verifications will not be possible.

6. Data Summary and Analyses

  1. Consult resources available to help in constructing life tables from the data collected2,8,11,31. An example life table is presented as Table 2.
    NOTE: Robust life table analyses require multiple independent life tables conducted over time and/or different sites. For a multivoltine insect like B. tabaci this could be multiple life tables over the course of a single season and/or multiple seasons and sites.
    1. Estimate real mortality (dx/l0) based on the number of insects established at the beginning of the generation.
      Real mortality=(dx/l0)
      Where dx is the number dying during stage x and l0 is the number of insects at the beginning of the generation. These mortality rates are additive and the sum of dx over stages estimates the total mortality rate for the generation (Table 2).
    2. Estimate apparent mortality within a stage (qx) based on the number of insect alive at the beginning of a specific stage (Table 2). Estimate stage-specific qx or factor within stage-specific qx. These rates are additive only within a stage.
    3. Determine marginal mortality using the formula:
      MB = dB/(1-dA)
      Where MB is the marginal rate of mortality factor B, dB is the apparent rate of mortality from factor B and dA is the apparent rate of mortality summed for all mortality factors that can outcompete factor B13,32 (Table 3).
      NOTE: For sessile insects like whiteflies and many other insects, the multiple causes of death within a particular life stage are not sequential. Instead they act contemporaneously and so estimation of marginal rates is required to accurately estimate stage-specific rates of mortality from any one cause32,34. For example, a parasitoid may attack a whitefly nymph. The parasitoid egg might hatch and the larvae may develop in the host. This activity, initially asymptomatic to the observer, does, or would most likely, kill the host insect and should be credited as the cause of death. But in some cases, a predator may attack this same nymph or the nymph may be dislodged from the leaf leading the observer to note the cause of death as predation or dislodgement. Marginal mortality corrects for this.
      1. Convert marginal stage-specific rates to k-values35 as k = -ln(1-M), where ln is the natural logarithm and M is the marginal mortality rate of interest. k-values are additive and this simplifies further analyses. k-values can be back-converted to proportional mortality rates by 1-e[-k].
    4. Estimate irreplaceable mortality as [1-e(-TotalK)] - [1-e(-{TotalK - kvaluei})].
      NOTE: This gives the portion of total generational mortality that would not be realized if a particular mortality factor was removed. For example, how much generational mortality might be lost if predation or parasitism were removed because of an insecticide spray? Irreplaceable mortality estimated in this way assumes that there is no density-dependence in mortality.
    5. Key factors
      1. Use a simple graphical analysis to plot the k-value for any one stage, or any one mortality factor (or one mortality factor within one stage) against the Total-K value for the entire generation (Total K = sum of all individual k-values).
        NOTE: The mortality factor that most closely mimics the pattern of Total-K the best is the key factor, the factor that contribute the most to changes in generational mortality35. A more quantitative method regresses individual k-values on Total-K and identifies the key factor as the one with the greatest slope value3.
    6. Test density dependence by regressing k-values for factors of interest on the natural log of insect population density measured independently (e.g.13). A significant positive slope suggests direct density dependence and a negative slope inverse dependence.
      NOTE: With additional life table information on adult survival and reproduction, many additional parameters (e.g., generation time, net reproductive rate, instantaneous rates of increase, life expectancy at a given stage, etc.) and analyses (matrix models and elasticity analyses36,37 can be conducted.

Access restricted. Please log in or start a trial to view this content.

Results

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

An example cohort is presented in Table 2 to show a typical presentation and calculation of life table results. The most useful data is captured in the marginal mortality rates for each factor within each stage. By converting these rates to k-values (protocol section 6), stage-specific mortality over all factors and factor-specific mortality over all stages can be easily estimated, as can total generational mortality. This also facilitates irreplaceable mortality, key-fac...

Access restricted. Please log in or start a trial to view this content.

Discussion

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

Typically, the development of life tables for multivoltine insects with broadly overlapping generations are constrained to a vertical approach where a population is sampled repeatedly over time and various graphical and mathematical techniques are then used to estimate recruitment to the various stages and infer rates of mortality from changing densities of the various life stages2. The strength of the approach here is that it navigates this limitation by isolating a group of immobile equal-aged i...

Access restricted. Please log in or start a trial to view this content.

Disclosures

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

The authors have nothing to disclose.

Acknowledgements

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

We thank D. Ashton, V. Barkley, K. Beimfohr, F. Bojorquez, J. Cantrell, G. Castro, R. Christensen, J. Fearn, C. Jara, D. Meade, G. Owens, L. Rodarte, D. Sieglaff, A. Sonoqui, M. Stefanek, B. Stuart, J. Trejo, A. Slade and E. Yescas for technical assistance. Partial support was provided by USDA-Agricultural Research Service, USDA-National Institute for Food and Agriculture Extension IPM Program and Pest Management Alternatives Special Projects, Cotton Incorporated, Arizona Cotton Growers Association, Cotton Foundations, USDA-CREES, NAPIAP (Western Region), and Western Region IPM Special Projects.

Access restricted. Please log in or start a trial to view this content.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Flagging tapeGempler, Janesville, Wisconsin USA52273Five colors
Manila merchandise tagsAmerican Tag Company, Pico Rivera, California USA12-104
Ultra fine point markerSanford, Bellwood, Illinois, USA451898Available at Office Max, Amazon
Peak Loupe 8XAdorama, New York, NY USA2018
Peak Loupe 15XAdorama, New York, NY USA19621

References

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,
  1. Deevey, E. S. Life tables for natural populations of animals. Quart. Rev. Biol. 22, 283-314 (1947).
  2. Southwood, T. R. E. Ecological Methods. , 2nd Ed, Chapman and Hall. (1978).
  3. Podoler, H., Rogers, D. A new method for the identification of key factors from life-table data. J Anim Ecol. 44, 85-114 (1975).
  4. Stiling, P. Density-dependent processes and key factors in insect populations. J Anim Ecol. 57, 581-593 (1988).
  5. Cornell, H. V., Hawkins, B. A. Survival patterns and mortality sources of herbivorous insects: some demographic trends. Am Nat. 145, 563-593 (1995).
  6. Morris, R. F. The interpretation of mortality data in studies on population dynamics. Can Entomol. 89, 49-69 (1957).
  7. Morris, R. F. Single-factor analysis in population dynamics. Ecology. 40, 580-588 (1959).
  8. Varley, G. C., Gradwell, G. R., Hassell, M. P. Insect Population Ecology: An Analytical Approach. , Blackwell Sci. Pub. London. 212(1973).
  9. Southwood, T. R. E., Reader, P. M. Population census data and key factor analysis for the viburnum whitefly, Aleurotrachelus jelinekii, on three bushes. J Anim Ecol. 45, 313-325 (1976).
  10. Royama, T. Fundamental concepts and methodologies for the analysis of animal population dynamics, with particular reference to univoltine speices. Ecol Monogr. 51, 473-493 (1981).
  11. Carey, J. R. The multiple decrement life table: a unifying framework for cause-of-death analysis in ecology. Oecologia. 78, 131-137 (1989).
  12. Hawkins, B. A., Mills, N. J., Jervis, M. A., Price, P. W. Is the biological control of insects a natural phenomenon? Oikos. 86, 493-506 (1999).
  13. Naranjo, S. E., Ellsworth, P. C. Mortality dynamics and population regulation in Bemisia tabaci. Entomol Exp Appl. 116, 93-108 (2005).
  14. Naranjo, S. E., Ellsworth, P. C. The contribution of conservation biological control to integrated control of Bemisia tabaci in cotton. Biol Control. 51, 458-470 (2009).
  15. Applied Demography for Biologist: With Special Emphasis on Insects. Carey, J. R. , Oxford, NY. (1993).
  16. Dinsdale, A., Cook, L., Riginos, C., Buckley, Y. M., De Barro, P. Refined global analysis of Bemisia tabaci (Hemiptera: Sternorrhyncha: Aleyrodoidea: Aleyrodidae) Mitochondrial cytochrome oxidase 1 to identify species level genetic boundaries. Ann Entomol Soc Am. 103, 196-208 (2010).
  17. Oliveira, M. R. V., Henneberry, T. J., Anderson, P. History, current status, and collaborative research projects for Bemisia tabaci. Crop Protect. 20, 709-723 (2001).
  18. Jones, D. R. Plant viruses transmitted by whiteflies. Eur J Plant Pathol. 109, 195-219 (2003).
  19. United States Department of Agriculture, Agricultural Research Service. Sticky Cotton - Causes, Impacts and Prevention. Hequet, E. F., Henneberry, T. J., Nichols, R. L. , Technical Bulletin No. 1915 (2007).
  20. Palumbo, J. C., Horowitz, A. R., Prabhaker, N. Insecticidal control and resistance management for Bemisia tabaci. Crop Protect. 20, 739-765 (2001).
  21. Horowitz, A. R., Kontsedalov, S., Khasdan, V., Ishaaya, I. Biotypes B and Q of Bemisia tabaci and their relevance to neonicotinoid and pyriproxyfen resistance. Arch Insect Biochem Physiol. 58, 216-225 (2005).
  22. Nauen, R., Denholm, I. Resistance of insect pests to neonicotinoid insecticides: current status and future prospects. Arch Insect Biochem Physiol. 58, 200-215 (2005).
  23. Naranjo, S. E., Ellsworth, P. C. Fifty years of the integrated control concept: moving the model and implementation forward in Arizona. Pest Manage Sci. 65, 1267-1286 (2009).
  24. Palumbo, J. C., Castle, S. J. IPM for fresh-market lettuce production in the desert southwest: the produce paradox. Pest Manage Sci. 65, 1311-1320 (2009).
  25. Ellsworth, P. C., Martinez-Carrillo, J. L. IPM for Bemisia tabaci: a case study from North America. Crop Protect. 20, 853-869 (2001).
  26. Naranjo, S. E., Ellsworth, P. C., CaƱas, L. Mortality and populations dynamics of Bemisia tabaci within a multi-crop system. , USDA Forest Service (2009).
  27. Naranjo, S. E. Long-term assessment of the effects of transgenic Bt cotton on the function of the natural enemy community. Environ Entomol. 34, 1211-1223 (2005).
  28. Naranjo, S. E. Establishment and impact of exotic Aphelinid parasitoids in Arizona: A life table approach. J Insect Sci. 8, 36(2008).
  29. Asiimwe, P., Ellsworth, P. C., Naranjo, S. E. Natural enemy impacts on Bemisia tabaci (MEAM1) dominate plant quality effects in the cotton system. Ecol Entomol. 41, 642-652 (2016).
  30. Naranjo, S. E. Survival and movement of Bemisia tabaci (Homoptera: Aleyrodidae) crawlers on cotton. Southwest Entomol. 32, 17-23 (2007).
  31. Bellows, T. S., Van Driesche, R. G., Elkinton, J. S. Life-table construction and analysis in the evaluation of natural enemies. Annu Rev Entomol. 37, 587-614 (1992).
  32. Buonaccorsi, J. P., Elkinton, J. S. Estimation of contemporaneous mortality factors. Res Popul Ecol. 32, 151-171 (1990).
  33. Royama, T. Evaluation of mortality factors in insect life table analysis. Ecol Monogr. 51, 495-505 (1981).
  34. Elkinton, J. S., Buonaccorsi, J. P., Bellows, T. S., Van Driesche, R. G. Marginal attack rate, k-values and density dependence in the analysis of contemporaneous mortality factors. Res. Pop. Ecol. 34, 29-44 (1992).
  35. Varley, G. C., Gradwell, G. R. Key factors in population studies. J Anim Ecol. 29, 399-401 (1960).
  36. Caswell, H. Matrix population models. , 2nd ed, Sinauer Associates, Inc. Publishers. (2001).
  37. Mills, N. J. Selecting effective parasitoids for biological control introductions: Codling moth as a case study. Biol Control. 34, 274-282 (2005).
  38. Foltyn, S., Gerling, D. The parasitoids of the aleyrodid Bemisia tabaci in Israel: development, host preference and discrimination of the aphelinid wasp Eretmocerus mundus. Entomol Exp Appl. 38, 255-260 (1985).
  39. Headrick, D. H., Bellows, T. S., Perring, T. M. Behaviors of female Eretmocerus sp nr californicus (Hymenoptera: Aphelinidae) attacking Bemisia argentifolii (Homoptera: Aleyrodidae) on sweet potato. Environ Entomol. 24, 412-422 (1995).
  40. Liu, T. X., Stansly, P. A. Oviposition, development, and survivorship of Encarsia pergandiella (Hymenoptera: Aphelinidae) in four instars of Bemisia argentifolii (Homoptera: Aleyrodidae). Ann Entomol Soc Am. 89, 96-102 (1996).
  41. Ardeh, M. J., deJong, P. W., vanLenteren, J. C. Selection of Bemisia nymphal stages for oviposition or feeding, and host-handling times of arrhenotokous and thelytokous Eretmocerus mundus and arrhenotokous E-eremicus. BioControl. 50, 449-463 (2005).
  42. Zang, L. S., Liu, T. X. Host-feeding of three parasitoid species on Bemisia tabaci biotype B and implications for whitefly biological control. Entomol Exp Appl. 127, 55-63 (2008).
  43. Hagler, J. R., Naranjo, S. E. Determining the frequency of heteropteran predation on sweetpotato whitefly and pink bollworm using multiple ELISAs. Entomol Exp Appl. 72, 63-70 (1994).
  44. Hagler, J. R., Naranjo, S. E. Qualitative survey of two Coleopteran predators of Bemisia tabaci (Homoptera, Aleyrodidae) and Pectinophora gossypiella (Lepidoptera, Gelechiidae) using a multiple prey gut content ELISA. Environ Entomol. 23, 193-197 (1994).
  45. Hagler, J. R., Jackson, C. G., Isaacs, R., Machtley, S. A. Foraging behavior and prey interactions by a guild of predators on various lifestages of Bemisia tabaci. J Insect Sci. 4, (2004).
  46. Price, J. F., Taborsky, D. Movement of immature Bemisia tabaci (Homoptera, Aleyrodidae) on poinsettia leaves. Florida Entomol. 75, 151-153 (1992).
  47. Simmons, A. M. Settling of crawlers of Bemisia tabaci (Homoptera : Aleyrodidae) on five vegetable hosts. Ann Entomol Soc Am. 95, 464-468 (2002).
  48. Royama, T. A fundamental problem in key factor analysis. Ecology. 77, 87-93 (1996).
  49. Stiling, P., Throckmorton, A., Silvanima, J., Strong, D. R. Does spatial scale affect the incidence of density dependence - A field test with insect parasitoids. Ecology. 72, 2143-2154 (1991).
  50. Hassell, M., Latto, J., May, R. Seeing the wood for the trees: detecting density dependence from existing life table studies. J Anim Ecol. 58, 883-892 (1989).
  51. Berryman, A. A. Population regulation, emergent properties, and a requiem for density dependence. Oikos. 99, 600-606 (2002).
  52. Sibly, R. M., Smith, R. H. Identifying key factors using lambda contribution analysis. J Anim Ecol. 67, 17-24 (1998).

Access restricted. Please log in or start a trial to view this content.

Reprints and Permissions

Request permission to reuse the text or figures of this JoVE article

Request Permission

Tags

Cohort AnalysisCotton SystemField MortalityEgg CohortsNymph CohortsHand LensNontoxic MarkerDissection Microscope

Related Articles