Forest disturbances play a critical role in ecosystem dynamics. However, the methods for quantifying these disturbances at broad scales may underestimate disturbances that affect individual trees. Utilizing individual tree variables may provide early disturbance detection that directly affects tree demographics and forest dynamics. The goals of this study were to (1) describe different methods for quantifying disturbances at individual tree and condition-level scales, (2) compare the differences between disturbance variables, and (3) provide a methodology for selecting an appropriate disturbance variable from national forest inventories for diverse applications depending on user needs. To achieve these goals, we used all the remeasurements available from the USDA Forest Inventory and Analysis (FIA) database since the start of the annual inventory for the lower 48 US states. Variables used included disturbance code, treatment code, agent of mortality, and damage code. Chi-square tests of independence were used to verify how the choice of the variable that represents disturbance affects its magnitude. Disturbed plots, as classified by each disturbance variable, were mapped to observe their spatial distribution. We found that the Chi-square tests were significant when using all the states and comparing each state individually, indicating that different results exist depending on which variable is used to represent disturbance. Our results will be a useful tool to help researchers measure the magnitude and scale of disturbance since the manner in which disturbances are categorized will impact forest management plans, national and international reports of forest carbon stocks, and sequestration potential under future global change scenarios.
Bibliographical noteFunding Information:
This work was supported by the US Department of Agriculture - Forest Service, Northern Research Station (project 14-JV-11242305-012), the Minnesota Agricultural Experiment Station (project MIN-42-101), and University of Minnesota Department of Forest Resources fellowship program.
We would like to express our gratitude to The USDA Forest Service Northern Research Station for the constant support on this project, the Forest Inventory and Analysis field crews that collected these data, to the University of Minnesota, Department of Forest Resources, and to The Student Writing Support at the University of Minnesota with a special thank you to Kim.
© 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
- Chi-square test
- Disturbance regime
- Ecosystem dynamics
- Forest health
- Landscape ecology
PubMed: MeSH publication types
- Journal Article