The use of species distribution models (SDM) is of increasing popularity when studying biological invasions, e.g. to assess the impact of climate change on invasive species, to prioritize conservation measures, or to study invasive evolutionary biology. SDM correlate known occurrences of species with environmental variables and predict a species' potential distribution on other geographies over space and time. Today, SDM are widely used to produce invasion risk maps by delineating probable risk areas based on climatic suitability for a species. These maps can guide early detection and rapid response measures. While recent developments in modelling approaches and wider availability of environment datasets have helped to create better and more accurate SDM, in many cases the used models ignore the associated underlying ecological processes for e.g. dispersal and biotic interactions and thus provide only an incomplete picture of invasion risks. In this paper, we present a review of the most common applications of SDM in invasive species management and provide an overview of possible solutions to various challenges like uncertainty and transferability associated with them. Based on our review we conclude that ways towards more accurate model outputs include fitting models with existing ecological knowledge (hybrid models), address uncertainty and biotic interactions and link species dispersal traits with projections of species distributions. In the future, work is required on developing more hybrid approaches and models that addresses both local diffusion and long distance movement of alien species.

Automatic identification and mapping of tree species is an essential task in forestry and conservation. However, applications that can geolocate individual trees and identify their species in heterogeneous forests on a large scale are lacking. Here, we assessed the potential of the Convolutional Neural Network algorithm, Faster R-CNN, which is an efficient end-to-end object detection approach, combined with open-source aerial RGB imagery for the identification and geolocation of tree species in the upper canopy layer of heterogeneous temperate forests. We studied four tree species, i.e., Norway spruce (Picea abies (L.) H. Karst.), silver fir (Abies alba Mill.), Scots pine (Pinus sylvestris L.), and European beech (Fagus sylvatica L.), growing in heterogeneous temperate forests. To fully explore the potential of the approach for tree species identification, we trained single-species and multi-species models. For the single-species models, the average detection accuracy (F1 score) was 0.76. Picea abies was detected with the highest accuracy, with an average F1 of 0.86, followed by A. alba (F1 = 0.84), F. sylvatica (F1 = 0.75), and Pinus sylvestris (F1 = 0.59). Detection accuracy increased in multi-species models for Pinus sylvestris (F1 = 0.92), while it remained the same or decreased slightly for the other species. Model performance was more influenced by site conditions, such as forest stand structure, and less by illumination. Moreover, the misidentification of tree species decreased as the number of species included in the models increased. In conclusion, the presented method can accurately map the location of four individual tree species in heterogeneous forests and may serve as a basis for future inventories and targeted management actions to support more resilient forests.

Shifts in tree species distributions caused by climatic change are expected to cause severe losses in the economic value of European forestland. However, this projection disregards potential adaptation options such as tree species conversion, shorter production periods, or establishment of mixed species forests. The effect of tree species mixture has, as yet, not been quantitatively investigated for its potential to mitigate future increases in production risks. For the first time, we use survival time analysis to assess the effects of climate, species mixture and soil condition on survival probabilities for Norway spruce and European beech. Accelerated Failure Time (AFT) models based on an extensive dataset of almost 65 000 trees from the European Forest Damage Survey (FDS) – part of the European‐wide Level I monitoring network – predicted a 24% decrease in survival probability for Norway spruce in pure stands at age 120 when unfavorable changes in climate conditions were assumed. Increasing species admixture greatly reduced the negative effects of unfavorable climate conditions, resulting in a decline in survival probabilities of only 7%. We conclude that future studies of forest management under climate change as well as forest policy measures need to take this, as yet unconsidered, strongly advantageous effect of tree species mixture into account.

Stand structure is a key attribute of forest ecosystems. Mixed-tree plantations are widely felt to be the appropriate option for providing a broad range of goods and environmental services and to reduce susceptibility to natural hazards. However, the debate continues whether mixed plantations can achieve greater financial return than monocultures can. In this study, mixed-species stands of conifers and hardwood species were analyzed in consideration of economically relevant factors. Growth performance and resistance to hazards and pests are widely noted in the literature and are of general economic interest. Thus meta-analyses of relevant studies were conducted to test the following hypotheses: (1) mixing tree species has no significant influence on growth performance or resistance against hazards and pests and, if refuted, (2) mixing tree species causes mainly negative effects on growth performance and resistance against hazards and pests. However, a positive impact of mixing tree species was proven for resistance against windthrow and pests. The meta-analysis on growth performance just as well indicates a positive effect of mixing tree species. Overall, these positive results underscore the need for a large number of additional studies to examine different silvicultural systems to develop optimal management prescriptions to benefit from positive interactions.

Multi-criteria decision analysis (MCDA) is a decision aid frequently used in the field of forest management planning. It includes the evaluation of multiple criteria such as the production of timber and non-timber forest products and tangible as well as intangible values of ecosystem services (ES). Hence, it is beneficial compared to those methods that take a purely financial perspective. Accordingly, MCDA methods are increasingly popular in the wide field of sustainability assessment. Hybrid approaches allow aggregating MCDA and, potentially, other decision-making techniques to make use of their individual benefits and leading to a more holistic view of the actual consequences that come with certain decisions. This review is providing a comprehensive overview of hybrid approaches that are used in forest management planning. Today, the scientific world is facing increasing challenges regarding the evaluation of ES and the trade-offs between them, for example between provisioning and regulating services. As the preferences of multiple stakeholders are essential to improve the decision process in multi-purpose forestry, participatory and hybrid approaches turn out to be of particular importance. Accordingly, hybrid methods show great potential for becoming most relevant in future decision making. Based on the review presented here, the development of models for the use in planning processes should focus on participatory modeling and the consideration of uncertainty regarding available information.