1. Nematode Sampling and Faunal Analysis Howard Ferris Department of Nematology University of California, Davis hferris@ucdavis.edu March, 2005

2. Objectives of monitoring/sampling for nematodes A. Assess risk of loss i) Determine presence or absence a. assessment of long-term risk - perennials b. virus-vectors c. root crops - direct damage. d. exotic pests ii) Determine population abundance - relative/absolute a. predict potential yield/damage b. assess rate of population change (+ or -) iii) Determine spatial patterns. a. pattern of potential loss b. partial treatment/management B. Faunistic studies i) Community structure and ecosystem analysis a. foodweb structure and function ii) Environmental impacts/quality /markers a. effects of disturbance and contaminants b. recovery from perturbation iii) Collections / surveys a. faunal inventories b. biodiversity studies

3. Environmental heterogeneity Zones and Gradients: texture structure temperature water O 2 CO 2 NO 3 NH 4 minerals Soil Food Webs – Environmental Effects on Structure Separate metacommunities?

4. Biological/Ecological Considerations A. Factors Affecting Microdistribution i) Life history strategies a. feeding/parasitism b. reproductive behavior c. motility ii) Food distribution a. crop spacing b. root morphology iii) Ecological requirements a. moisture b. temperature (magnitude and stability) c. oxygen B. Factors Affecting Macrodistribution i) Crop history, management, field usage a. crop sequence b. spatial arrangement of previous crops ii) Age of infestation a. time to spread from a point source iii) Edaphic conditions a. soil texture patterns iv) Drainage patterns a. soil moisture levels b. soil aeration

6. Alternative Sampling Devices

7. Efficiency and Reliability - Optimal Sampling Methodology A. Pattern i) Organism moves to sampler a. only over small distances in soil organisms b. to roots of bioassay plants or to CO 2 attractants. ii) Sampler moves to organism a. core sampling - aggregate samples b. symptom assessment, e.g. gall ratings - where possible iii) Field Stratification - based on macrodistribution parameters a. minimizes variance within each stratum b. increases confidence in estimate of mean c. allows determination of spatial pattern B. Timing i) To maximize probability of achieving objectives a. detect presence when populations highest b. greatest precision when lowest? - but may be many misses! ii) To allow evaluation and management decision a. prior to planting b. end of growing season, following treatment, etc.

10. As sample units become larger, perception of aggregated patterns: aggregated > random > uniform

11. Some of those involved…. Dan Ball Larry Duncan Pete Goodell Joe Noling Diane Alston Sally Schneider Lance Beem Nematode Thresholds and Damage Levels

12. Seinhorst Damage Function Y=m+(1-m)z (P i -T) Y=relative yield m=minimum yield Z=regression parameter P i =population level T=tolerance level Based on preplant population levels – measured or predicted from overwinter survival rates

13. Thresholds and Expected Yield Loss Meloidogyne incognita, J2/250 cc soil; adjusted for extraction efficiency Expected % yield loss at different preplant nematode densities CropThreshold125102050100200 Bell Pepper2500000258 Cantaloupe400137173046 Carrot0125916293740 Chile Pepper1500003142430 Cotton220000061527 Cowpea5200000068 Potato7000415344751 Snapbean500013101829 Squash0351223417493100 Sugarbeet0001125810 Sweetpotato0124815304351 Tomato16000003714

14. Expected Damage Meloidogyne chitwoodi; summer crop potato; Klamath Basin Fall population levels; adjusted for extraction efficiency Expected % tuber blemish at different fall nematode densities J2/250 cc125102050100200500 % Blemish3457812151825

15. Thresholds and Expected Yield Loss CultivarSoilLocation(T)oleranceZm US-H9clayImperial1000.998860 US-H9loamSJV/Idaho3000.999760 Heterodera schachtii, eggs/100g soil Sugarbeets CultivarSoilLocationThreshold501002005001000 US-H9clayImperial10000113764 US-H9loamSJV/Idaho300000515 Expected % yield loss at different preplant nematode densities

16. Soil Food Webs - Function Decomposition of organic matter Cycling of minerals and nutrients Reservoirs of minerals and nutrients Redistribution of minerals and nutrients Sequestration of carbon Degradation of pollutants, pesticides Modification of soil structure Community self-regulation Biological regulation of pest species

17. Soil Food Web Structure - the need for indicators

18. The Nematode Fauna as a Soil Food Web Indicator Herbivores Bacterivores Fungivores Omnivores Predators

19. Functional Diversity of Nematodes

20. Rhabditidae Panagrolaimidae etc.  Short lifecycle  Small/ Mod. body size  High fecundity  Small eggs  Dauer stages  Wide amplitude  Opportunists  Disturbed conditions Aporcelaimidae Nygolaimidae etc.  Long lifecycle  Large body size  Low fecundity  Large eggs  Stress intolerant  Narrow amplitude  Undisturbed conditions Enrichment Indicators Structure Indicators Cephalobidae Aphelenchidae, etc.  Moderate lifecycle  Small body size  Stress tolerant  Feeding adaptations  Present in all soils Basal Fauna

21. Ba 2 Fu 2 Ba 1 Ba 3 Fu 3 Ca 3 Ba 4 Fu 4 Ca 4 Om 4 Ba 5 Fu 5 Ca 5 Om 5 Enriched Structured Basal condition Structure index Enrichment index Disturbed N-enriched Low C:N Bacterial Conducive Maturing N-enriched Low C:N Bacterial Regulated Matured Fertile Mod. C:N Bact./Fungal Suppressive Degraded Depleted High C:N Fungal Conducive Testable Hypotheses of Food Web Structure and Function Ferris et al. (2001)

22. 0 50 100 050100 Structure Index Enrichment Index Prune Orchards Yuba Co. Mojave Desert Tomato Systems Yolo Co. Redwood Forest and Grass Mendocino Co. Trajectory Analysis of Some California Soil Systems

23. Carbon Pathways and Pools Omnivory Decomposition Herbivory Bacterial Fungal

26. 0 50 100 050100 0 50 100 050100 Structure index Enrichment index Sampled 2000 Organically-managed for 12 years Structure index Sampled 2001 After Deep Tillage How Fragile is the System? Berkelmans et al. (2003)

28. Bongers, T., H. Ferris. 1999. Nematode community structure as a bioindicator in environmental monitoring. Trends Ecol. Evol. 14, 224-228. Duncan, L. W. and H. Ferris. 1983. Effects of Meloidogyne incognita on cotton and cowpeas in rotation. Proceedings of the Beltwide Cotton Production Research Conference: 22-26. Ferris, H. 1984. Probability range in damage predictions as related to sampling decisions. Journal of Nematology 16:246-251. Ferris, H., D. A. Ball, L. W. Beem and L. A. Gudmundson. 1986. Using nematode count data in crop management decisions. California Agriculture 40:12-14. Ferris, H., T. Bongers, R. G. M. de Goede. 2001. A framework for soil food web diagnostics: extension of the nematode faunal analysis concept. Appl. Soil Ecol. 18, 13-29. Ferris, H., P. B. Goodell, M. V. McKenry. 1981. Sampling for nematodes. California Agriculture 35:13-15. Ferris, H., M.M. Matute. 2003. Structural and functional succession in the nematode fauna of a soil food web. Appl. Soil Ecol. 23:93-110. Tenuta, M., H. Ferris. 2004. Relationship between nematode life-history classification and sensitivity to stressors: ionic and osmotic effects of nitrogenous solutions. J. Nematol. 36:85-94. More information: http://plpnemweb.ucdavis.edu/nemaplex/nemaplex.htm Some References