Forest Plot
Forest Plot - Related Videos
09:16
Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils
0 Views •
Cited by 19 •
2016
Repeated soil sampling has recently been shown to be an effective way to monitor forest soil change over years and decades. To support its use, a protocol is presented that synthesizes the latest information on soil resampling methods to aid in the design and implementation of successful soil monitoring programs.
08:16
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
0 Views •
Cited by 1 •
2025
Recent advancements in remotely piloted aircraft systems (RPAS) allow sub-meter resolution, ideal for forest recovery monitoring. Integrating artificial intelligence (AI) enables deeper insights from large remotely sensed datasets. This protocol improves monitoring by supporting more efficient assessment and management of forested lands recovering from disturbance.
07:11
Sampling Soils in a Heterogeneous Research Plot
0 Views •
Cited by 27 •
2019
The traditional soil-sampling procedure determines the number of soil samples arbitrarily. Here, we provide a simple yet efficient clustered soil-sampling design to demonstrate soil spatial heterogeneity and quantitatively determine the number of soil samples required and the associated sampling accuracy.
02:34
Arrhenius Plots
0 Views •
2020
The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules. The Arrhenius equation can be used to...
08:10
Precision Milling of Carbon Nanotube Forests Using Low Pressure Scanning Electron Microscopy
0 Views •
2017
Low pressure scanning electron microscopy in a water vapor ambient is used to machine nanoscale to microscale features in carbon nanotube forests.
FAQs
The marker’s horizontal position represents the estimated effect for that study on the graph’s effect scale. Comparing positions shows whether estimates are similar, separated, or distributed across different values. Interpretation depends on the reference line and the effect measure used, so the location should be considered together with the confidence interval rather than viewed in isolation.
Marker size can represent a study’s weight in the analysis. Larger markers identify studies that contribute more strongly to the pooled estimate, whereas smaller markers indicate less influence. This visual cue helps readers distinguish the relative contribution of studies without treating every estimate as equally informative.
The line of no effect provides a reference for judging whether an interval includes the value representing no observed effect. A confidence interval that crosses this line indicates that the study’s uncertainty range includes that reference value. Intervals that remain on one side show a more clearly separated estimate, while their width conveys precision.
When study markers and confidence intervals are widely separated, the results may be less consistent and may indicate greater heterogeneity. Closely grouped estimates suggest more similarity, although interval width also affects the visual impression. A forest plot therefore helps identify patterns requiring closer statistical or substantive examination rather than proving why studies differ.
A basic forest plot requires an effect estimate and an uncertainty interval for each study or group. The display may also include study weights, labels, a line of no effect, and a pooled estimate summarizing the combined evidence. These elements allow the graph to show individual results alongside their relative contribution and overall summary.
The pooled estimate provides a single summary of the combined evidence, while the individual study markers show how that summary relates to the underlying results. Comparing the pooled marker with the separate estimates helps readers assess whether the overall finding reflects a consistent pattern or masks meaningful differences among studies.
Forest plots are particularly useful in systematic reviews, clinical research, epidemiology, and evidence-based decision-making. They communicate quantitative findings in a compact visual form while supporting assessment of consistency, precision, and potential heterogeneity. This makes them useful when readers need to compare results across studies and understand both individual and combined evidence.