ASAS Public Policy Newsletter
Emerging Technology for Quantifying Diet Composition of Grazing Animals
Dr. John ‘Derek’ Scasta, University of Wyoming, jscasta@uwyo.edu
Determining what plants grazing animals are eating has been a persistent challenge for animal and rangeland scientists (Holechek et al. 1982). This challenge not only applies to important livestock species such as cattle, sheep, and horses but also wildlife – all of which often or always inhabit large extensive rangelands with complex plant communities.
Early attempts to quantify diets of grazing animals used direct observations of animal bites (McMahan 1964). Direct observation has many logistical problems including being close enough to the animal, human presence altering grazing behavior, animals may eat multiple plant species in a single bite, being able to identify plant species correctly, and only 1 animal observed per technician at any point in time. Eventually more invasive methods were developed including surgical stomach analysis and rumen or esophageal fistulation (Olson 1991). These surgical techniques also have limitations including the mastication of plant material may render it un-identifiable, a relatively small sample size due to complex surgeries, time intensive methods, and secondary complications from surgery.
Scientists then developed an interest in non-invasive and post-ingestive techniques and the use of fecal microhistology which was not a new technique, dating back to the 1930’s (Bartolome et al. 1995), but went through a period of refinement and adoption from the 1950s through the 1990s (Croker 1959). Microhistology also has limitations including plant material in feces may not be proportional to that consumed by the animals due to differential digestion, trained observers are required, observer bias, very time intensive, errors have been noted, and differential mastication may hinder identification.
To move beyond human error and limitations, scientists have employed the use of technologies such as near infrared reflectance spectroscopy (NIRS) or stable isotopes (δ13C and δ15N) but with limited success in identifying multiple species at a time (Landau et al. 2004; Crawford et al. 2008). These technologies have been successful for identifying groups of plants (i.e., different functional groups or morphological groups such as grasses or shrubs, or different photosynthetic pathways such as C3, C4, or CAM [cool-season, warm-season, or cacti]) but have struggled at the species level.
More recently, there has been an emergence of DNA metabarcoding of fecal material using a single-locus identification approach (Scasta et al. 2019). DNA metabarcoding of fecal material to determine botanical composition focuses on the trnL intron which is located on the plant chloroplast using c-h primers and estimates species diet composition through associations to plant species-specific dietary protein content. Advantages of this emerging technique include it being non-invasive, ability to have large sample sizes, automation of reading numerous DNA sequences at a single time, and ability to align with large reference libraries. There are some known issues with DNA metabarcoding including incorrect identifications, a need for verification of results with field botanical data, improvement of reference libraries that include voucher specimens, and understanding how chloroplast density on different plant functional or morphological groups influences relative abundance of results. In short, improved integration of bioinformatics with lab results are needed to improve the applications of fecal DNA metabarcoding but the potential applications are exciting and promising.
References
Bartolome, J., Franch, J., Gutman, M., & Seligman, N. A. G. (1995). Physical factors that influence fecal analysis estimates of herbivore diets. Journal of Range Management, 48(3), 267-270.
Crawford, K., Mcdonald, R. A., & Bearhop, S. (2008). Applications of stable isotope techniques to the ecology of mammals. Mammal Review, 38(1), 87-107.
Croker, B. H. (1959). A method of estimating the botanical composition of the diet of sheep. New Zealand Journal of Agricultural Research, 2(1), 72-85.
Holechek, J. L., Vavra, M., & Pieper, R. D. (1982). Botanical compostion of determination of range herbivore diets. A review Grazing animals, forage resources. Journal of Range Management, 35(3), 309-315.
Landau, S., Glasser, T., Dvash, L., & Perevolotsky, A. (2004). Faecal NIRS to monitor the diet of Mediterranean goats. South African Journal of Animal Science, 34(5), 76-80.
McMahan, C. A. (1964). Comparative food habits of deer and three classes of livestock. The Journal of Wildlife Management, 798-808.
Olson, K. C. (1991). Diet sample collection by esophageal fistula and rumen evacuation techniques. Journal of Range Management, 44(5), 515-519.
Scasta, J. D., Jorns, T., Derner, J. D., Lake, S., Augustine, D. J., Windh, J. L., & Smith, T. L. (2019). Validation of DNA metabarcoding of fecal samples using cattle fed known rations. Animal Feed Science and Technology, 255, 114219.
