Insights in forest structural diversity indicators with machine learning: what is indicated?

Indicator choice is a crucial step in biodiversity assessments. Forest inventories have the potential to overcome data deficits for biodiversity monitoring on large spatial scales which is fundamental to reach biodiversity policy targets. Structural diversity indicators were taken from information theory to describe forest spatial heterogeneity. Their indicative value for forest stand variables is largely unknown. This case study explores these indicator-indicandum relationships in a lowland, European beech (Fagus sylvatica) dominated forest in Austria, Central Europe. We employed five indicators as surrogates for structural diversity which is an important part of forest biodiversity i.e., Clark & Evans-, Shannon, Stand Density, Diameter Differentiation Index, and Crown Competition factor. The indicators are evaluated by machine learning, to detect statistic inter-correlation in an indicator set and the relationship to twenty explanatory stand variables and five variable groups on a landscape scale. Using the R packages randomForest, VSURF, and randomForest Explainer, 1555 sample plots are considered in fifteen models. The model outcome is decisively impacted by the type and number of explanatory variables tested. Relationships to interval-scaled, common stand characteristics can be assessed most effectively. Variables of stand age & density' are disproportionally indicated by our indicator set while other forest stand characteristics relevant to biodiversity are neglected. Within the indicator set, pronounced inter-correlation is detected. The Shannon Index indicates the overall high-est, the Stand Density Index the lowest number of stand characteristics. Machine learning proves to be a useful tool to overcome knowledge gaps and provides additional insights in indicator-indicandum relationships of structural diversity indicators.

Automated summary

Indicator choice is crucial for biodiversity assessments. Forest inventories can help overcome data deficits and achieve biodiversity policy targets. This study explores the relationships between structural diversity indicators and forest stand variables. Machine learning is used to identify inter-correlations and assess the effectiveness of different variables.