characterization of determinants with an agent-based land use model

Wood is a limited resource which is exposed to a continuously growing global demand not least because of a politically fostered bioenergy use. One approach to master the challenge to sustainably meet this increasing wood demand is short rotation forestry (SRF). However, SRF is only gradually evolving and it is not fully understood which determinants hamper its expansion. This study provides theoretical insights into economic and environmental determinants of an SRF expansion and their interplay. This assessment requires the incorporation of farmers' decision-making based on an explicit investment appraisal. Therefore, we use an agent-based model to depict the decision- making of profit-maximizing farmers facing the choice between SRF, the cultivation of conventional annual agricultural crops and abstaining from cultivation (fallow land). The land use decisions are influenced by general economic determinants, such as market prices for wood and annual crops, and by site-dependent determinants, such as the environmental site quality. We found that the willingness to pay for SRF-based products and for annual crops most strongly influences the coverage of SRF in the landscape. SRF will in most cases be established on sites with low productivity. However, a decrease in the willingness to pay for annual crops will lead to a reallocation of SRF plantations to sites with higher productivity. Furthermore, our model results indicate that the impact of the distance to processing plants on farmers' decisions strongly depends on general economic determinants and the given spatial structure of the underlying natural landscape. Analysing the relative importance of different determinants of an SRF expansion, this study gives insights into the approach of using SRF to sustainably meet the growing wood demand. Moreover, these insights are taken as a starting point for the design of effective government interventions to promote SRF

Standardised and transparent model descriptions for agent-based models:Current status and prospects

Agent-based models are helpful to investigate complex dynamics in coupled humanenatural systems. However, model assessment, model comparison and replication are hampered to a large extent by a lack of transparency and comprehensibility in model descriptions. In this article we address the question of whether an ideal standard for describing models exists. We first suggest a classification for structuring types of model descriptions. Secondly, we differentiate purposes for which model descriptions are important. Thirdly, we review the types of model descriptions and evaluate each on their utility for the purposes. Our evaluation finds that the choice of the appropriate model description type is purposedependent and that no single description type alone can fulfil all requirements simultaneously. However, we suggest a minimum standard of model description for good modelling practice, namely the provision of source code and an accessible natural language description, and argue for the development of a common standard

Upscaling in socio-environmental systems modelling: Current challenges, promising strategies and insights from ecology

Sustainability challenges in socio-environmental systems (SES) are inherently multiscale, with global-level changes emerging from socio-environmental processes that operate across different spatial, temporal, and organisational scales. Models of SES therefore need to incorporate multiple scales, which requires sound methodologies for transferring information between scales. Due to the increasing global connectivity of SES, upscaling – increasing the extent or decreasing the resolution of a modelling study – is becoming progressively more important. However, upscaling in SES models has received less attention than in other fields (e.g., ecology or hydrology) and therefore remains a pressing challenge. To advance the understanding of upscaling in SES, we take three steps. First, we review existing upscaling approaches in SES as well as other disciplines. Second, we identify four main challenges that are particularly relevant to upscaling in SES: 1) heterogeneity, 2) interactions, 3) learning and adaptation, and 4) emergent phenomena. Third, we present an approach that facilitates the transfer of existing upscaling methods to SES, using two good practice examples from ecology. To describe and compare these methods, we propose a scheme of five general upscaling strategies. This scheme builds upon and unifies existing schemes and provides a standardised way to classify and represent existing as well as new upscaling methods. We demonstrate how the scheme can help to transparently present upscaling methods and uncover scaling assumptions, as well as to identify limits for the transfer of upscaling methods. We finish by pointing out research avenues on upscaling in SES to address the identified upscaling challenges

Model-based extrapolation of ecological systems under future climate scenarios: The example of Ixodes ricinus ticks

Models can be applied to extrapolate consequences of climate change for complex ecological systems in the future. The acknowledged systems\u27 behaviour at present is projected into the future considering climate projection data. Such an approach can be used to addresses the future activity and density of the castor bean tick Ixodes ricinus, the most widespread tick species in Europe. It is an important vector of pathogens causing Lyme borreliosis and tick-borne encephalitis. The population dynamics depend on several biotic and abiotic factors. Such complexity makes it difficult to predict the future dynamics and density of I. ricinus and associated health risk for humans. The objective of this study is to force ecological models with high-resolution climate projection data to extrapolate I. ricinus tick density and activity patterns into the future. We used climate projection data of temperature, precipitation, and relative humidity for the period 1971-2099 from 15 different climate models. Tick activity was investigated using a climate-driven cohort-based population model. We simulated the seasonal population dynamics using climate data between 1971 and 2099 and observed weather data since 1949 at a specific location in southern Germany. We evaluated derived quantities of local tick ecology, e.g. the maximum questing activity of the nymphal stage. Furthermore, we predicted spatial density changes by extrapolating a German-wide tick density model. We compared the tick density of the reference period (1971-2000) with the counter-factual densities under the near-term scenario (2012-2041), mid-term scenario (2050-2079) and long-term scenario (2070-2099). We found that the nymphal questing peak would shift towards early seasons of the year. Also, we found high spatial heterogeneity across Germany, with predicted hotspots of up to 2,000 nymphs per 100 m2 and coldspots with constant density. As our results suggest extreme changes in tick behaviour and density, we discuss why caution is needed when extrapolating climate data-driven models into the distant future when data on future climate drive the model projection