Key message: A validation convention can be established for forest management optimization models. It consists of (1) the delivery of face validation, (2) performing at least one other validation technique, and (3) an explicit discussion of how the optimization model fulfills the stated purpose. Validation by potential users or external experts is of high importance. Context: Optimization modeling has long assisted the management of forest ecosystems, but the credibility of these models has always been debated with criticisms concerning data quality, failures to include relevant processes in the scope of models, and the inclusion of unrealistic assumptions. Validation is widely considered to be crucial to establishing the credibility of models in general, but how to validate optimization models in particular represents a permanent question generally in operations research. Aims: We aim to synthesize practical recommendations for the development of validation frameworks in the optimization modeling for forest management. Methods: We selected a sample of 46 studies devoted to optimization models to be applied in practice, analysed the contents with respect to validation, and provided a critical review. Results: We (1) clarified the meaning and usage of different validation-related terms that are commonly encountered in the literature, (2) identified and categorised the various methods and frameworks that are used to demonstrate model credibility, and (3) derived organizing principles that helped to suggest improvements in validation frameworks. Conclusions: A practical validation convention can be established and we suggest the convention to consist of three stages. By providing structured and consistent information about validation processes, researchers in forest management optimization can better demonstrate the credibility of their work to readers and potential users.

Ungulate browsing has a major impact on the composition and structure of forests. Repeatedly conducted, large-scale regeneration inventories can monitor the extent of browsing pressure and its impacts on forest regeneration development. Based on the respective results, the necessity and extent of wildlife management activities such as hunting, fencing, etc., can be identified at a landscape scale. However, such inventories have rarely been integrated into wildlife management decision making. In this article, we evaluate a regeneration inventory method which was carried out in the Bavarian Forest National Park between 2007 and 2018. We predict the browsing impact by calculating browsing probabilities using a logistic mixed effect model. To provide wildlife managers with feedback on their activities, we developed a test which can assess significant changes in browsing probability between different inventory periods. To find the minimum observable browsing probability change, we simulated ungulate browsing based on the data of a potential browsing indicator species (Sorbus aucuparia) in the National Park. Sorbus aucuparia is evenly distributed, commonly found, selectively browsed and meets the ecosystem development objectives in our study area. We were able to verify a browsing probability change down to ±5 percentage points with a sample size of about 1,000 observations per inventory run. In view of the size of the National Park and the annual fluctuations in browsing pressure, this estimation accuracy seems sufficient. In seeking the maximal cost-efficiency, we were able to reduce this sample size in a sensitivity analysis by about two thirds without severe loss of information for wildlife management. Based on our findings, the presented inventory method combined with our evaluation tool has the potential to be a robust and efficient instrument to assess the impact of herbivores that are in the National Park and other regions.

Intensive ungulate browsing significantly impacts forests worldwide. However, it is usually not single browsing events that lead to sapling mortality, but the little-researched interactions of browsed saplings with their biotic and abiotic environment. (I) Our objective was to assess the impact of ungulate browsing on the growth of young saplings relative to other environmental factors by utilizing their height increment as a sensitive measure of vitality to indicate their status. (II) Furthermore, we aimed to identify factors affecting ungulate browsing at our study sites, assessed as browsing probabilities, and identify effective mitigation measures for browsing impact. We analyzed an extensive sapling dataset of 248 wildlife exclosures, which were erected in 2016 in beech dominated forests across Germany and assessed annually until 2020. (I) Browsing probability and light availability were the most influential parameters for selectively browsed, admixed tree species (e.g., sycamore maple). Height increment showed abrupt setbacks, which caused a permanent collapse of growth when browsing exceeded a certain level. However, light availability enhanced height increment. (II) An increase in deer harvest reduced the browsing probability of selectively browsed species considerably. We conclude that the growth-inhibiting effect of ungulate browsing is a multifactorial phenomenon, which can be mitigated by silvicultural management and efficient hunting strategies.

How to simultaneously combat biodiversity loss and maintain ecosystem functioning while improving human welfare remains an open question. Optimization approaches have proven helpful in revealing the trade‐offs between multiple functions and goals provided by land‐cover configurations. The R package optimLanduse provides tools for easy and systematic applications of the robust multiobjective land‐cover composition optimization approach of Knoke et al. (2016). The package includes tools to determine the land‐cover composition that best balances the multiple functions a landscape can provide, and tools for understanding and visualizing the reasoning behind these compromises. A tutorial based on a published dataset guides users through the application and highlights possible use‐cases. Illustrating the consequences of alternative ecosystem functions on the theoretically optimal landscape composition provides easily interpretable information for landscape modelling and decision‐making. The package opens the approach of Knoke et al. (2016) to the community of landscape modellers and planners and provides opportunities for straightforward systematic or batch applications.