Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection

Jari Hynynen,Sauli Valkonen,Simone Bianchi,Mari Myllymaki,Jouni Siipilehto,Hannu Salminen

doi:10.3390/f11121338

Abstract

Background and Objectives: Continuous cover forestry is of increasing importance, but operational forest growth models are still lacking. The debate is especially open if more complex spatial approaches would provide a worthwhile increase in accuracy. Our objective was to compare a nonspatial versus a spatial approach for individual Norway spruce tree growth models under single-tree selection cutting. Materials and Methods: We calibrated nonlinear mixed models using data from a long-term experiment in Finland (20 stands with 3538 individual trees for 10,238 growth measurements). We compared the use of nonspatial versus spatial predictors to describe the competitive pressure and its release after cutting. The models were compared in terms of Akaike Information Criteria (AIC), root mean square error (RMSE), and mean absolute bias (MAB), both with the training data and after cross-validation with a leave-one-out method at stand level. Results: Even though the spatial model had a lower AIC than the nonspatial model, RMSE and MAB of the two models were similar. Both models tended to underpredict growth for the highest observed values when the tree-level random effects were not used. After cross-validation, the aggregated predictions at stand level well represented the observations in both models. For most of the predictors, the use of values based on trees’ height rather than trees’ diameter improved the fit. After single-tree selection cutting, trees had a growth boost both in the first and second five-year period after cutting, however, with different predicted intensity in the two models. Conclusions: Under the research framework here considered, the spatial modeling approach was not more accurate than the nonspatial one. Regarding the single-tree selection cutting, an intervention regime spaced no more than 15 years apart seems necessary to sustain the individual tree growth. However, the model’s fixed effect parts were not able to capture the high growth of the few fastest-growing trees, and a proper estimation of site potential is needed for uneven-aged stands.

Highlights

Continuous cover forestry (CCF) is a silvicultural system that avoids the use of clear-felling and maintains a continuity of woodland conditions across the site [1]
Under the nonspatial approach, we considered the basal area removed during the last intervention relative to the total stand basal area prior to the cutting; and the sum of the trees’ height removed relative to the total
For a full forest growth simulation, the modeling of processes such as regeneration could for a full forest growth simulation, the modeling of processes such as regeneration could still require still require spatial information, since they may be more sensible to local variation in stand density spatial information, since they may be more sensible to local variation in stand density (e.g., [61])

Summary

Introduction

Continuous cover forestry (CCF) is a silvicultural system that avoids the use of clear-felling and maintains a continuity of woodland conditions across the site [1]. In Fennoscandia, [3] highlighted the lack of research data and of adequate growth models for CCF when compared with even-aged plantation forestry, or rotation forestry (RF). Individual tree modeling has long been recognized as the best approach for simulating the growth of irregular stands [4,5]. Continuous cover forestry is of increasing importance, but operational forest growth models are still lacking. Our objective was to compare a nonspatial versus a spatial approach for individual Norway spruce tree growth models under single-tree selection cutting. Materials and Methods: We calibrated nonlinear mixed models using data from a long-term experiment in Finland (20 stands with 3538 individual trees for 10,238 growth measurements). We compared the use of nonspatial versus spatial predictors to describe the competitive pressure and its release after cutting.

Objectives

Methods

Results

Discussion

Conclusion