Age class structure

Age class structure refers to the distribution of individuals in a population through different age groups. This is one tool used in fisheries and wildlife management as part of population assessment and modeling.

Using Age Class Structure in Wildlife Management

Population Pyramids, one way to visualize a population's age class structure.

Age class structures can be used to model population structures of many species including vertibrates, invertibrates, and vegetation. These models allow for the prediction of growth or decline in a population based on current conditions or future management practices. Age class structures can be used to help focus management on a certain age class to obtain the desires population size outcomes.

For example, when managing a population of white tail deer targeting a specific age and sex can alter the population pyramid type to either increase, decrease, or stabalize poplation growth. If you start off with a rapid growth age class structure, then targetting the juvenoles who are not reproductive yet will help stabalize the population to a slower growth or no growth. This will make the number of juvenile females to closer the number of females who are aging out of the reproductive age. Targetting a specific sex will also alter the age class data. If looking to reduce thr population then targeting hunting efforts on females will have a more impactful change.^[1] Removal of older individuals and males will increase the population by opening up resources for reproductive females and juveniles.

Analyzing fisheries age class structure

Age can be determined by counting growth rings in fish scales, otoliths, cross-sections of fin spines for species with thick spines such as triggerfish,^[2] or teeth for a few species.^[3][4] Each method has its merits and drawbacks. Fish scales are easiest to obtain, but may be unreliable if scales have fallen off the fish and new ones grown in their places.^[5] Fin spines may be unreliable for the same reason, and most fish do not have spines of sufficient thickness for clear rings to be visible. Otoliths will have stayed with the fish throughout its life history,^[2] but obtaining them requires killing the fish.^[6] Also, otoliths often require more preparation before ageing can occur.^[5]

An example of using age class structure to learn about a population is a regular bell curve for the population of 1-5 year-old fish with a very low population for the 3-year-olds. An age class structure with gaps in population size like the one described earlier implies a bad spawning year 3 years ago in that species.^[7] Often fish in younger age class structures have very low numbers because they were small enough to slip through the sampling nets, and may in fact have a very healthy population.^[8] This could skew class structure data and result in imaccurate management techniques.

Age class structures can be used to determine when wildfires occurred within a forest population.

Age Class Structures After Wildfires

Aging trees is perforned throough several methods, the first being to count the trees annual growth rings and the second is to calculate using the growth factor and circumference. After a wildfire, depedning on the severity, some of the forest will experience a loss in the population of several species. This interference can drastically alter an age class structure. If the fire was low intensity and burned low to the ground killing only young trees, then the age strucutre would be heavily skewed towards having many older mature trees and very few young trees. Wild fires can alter age class structures ^[9] by removing individuals in certain age classes and altering the type of population pyramid the species is experiencing. This shift in age class structures can also help predict how quickly the forest will rebound from the disturbance.

See also

• Identification of aging in fish
• Population pyramid
• Population dynamics of fisheries

References

1. Rughetti, Marco (2016-11-01). "Age structure: an indicator to monitor populations of large herbivores". Ecological Indicators. Navigating Urban Complexity: Advancing Understanding of Urban Social – Ecological Systems for Transformation and Resilience. 70: 249–254. doi:10.1016/j.ecolind.2016.06.023. ISSN 1470-160X.
2. O’Sullivan, Sandra (2007). Fisheries Long Term Monitoring Program. Brisbane, Australia: Queensland Department of Primary Industries and Fisheries.
3. Field, I.C., Meekan, M.G. & Bradshaw, C.J.A. (2009). Development of non-lethal methods for determining age and habitat use of sawfishes from northern Australia. Australia: Australian Department of the Environment and Energy.
4. Helfman, Gene; Collette, Bruce B.; Facey, Douglas E.; Bowen, Brian W. (2009-04-09). The Diversity of Fishes - Biology, Evolution, Andecology 2E (PDF). Oxford: Wiley-Blackwell UK. ISBN 9781405124942.
5. "Manual of Fisheries Science Part 2 - Methods of Resource Investigation and their Application". www.fao.org. Retrieved 2018-03-24.
6. Lux, Fred E. "Age Determination in Fishes". Washington, DC, United States: United States Bureau of Commercial Fisheries. Available at http://spo.nmfs.noaa.gov/Fishery%20Leaflets/leaflet488.pdf. Accessed 24/03/2018.
7. Botsford, Louis W.; Holland, Matthew D.; Samhouri, Jameal F.; White, J. Wilson; Hastings, Alan (2011). "Importance of age structure in models of the response of upper trophic levels to fishing and climate change". ICES Journal of Marine Science. 68 (6): 1270–1283. doi:10.1093/icesjms/fsr042.
8. Haines, Terry A. (1990). Intensive Studies of Stream Fish Populations in Maine. Washington, D.C., USA: U.S. Environment Protection Agency, Office of Acid Deposition, Environmental Monitoring and Quality Assurance. p. 17.
9. Huff, Mark H. (May 1995). "Forest Age Structure and Development Following Wildfires in the Western Olympic Mountains, Washington". Ecological Applications. 5 (2): 471–483. doi:10.2307/1942037. ISSN 1051-0761.