Implementing spatially explicit wind-driven seed and pollen dispersal in the individual-based larch simulation model: LAVESI-WIND 1.0

Abstract

Abstract. It is of major interest to estimate the feedback of arctic ecosystems to the global warming we expect in upcoming decades. The speed of this response is driven by the potential of species to migrate, tracking their climate optimum. For this, sessile plants have to produce and disperse seeds to newly available habitats, and pollination of ovules is needed for the seeds to be viable. These two processes are also the vectors that pass genetic information through a population. A restricted exchange among subpopulations might lead to a maladapted population due to diversity losses. Hence, a realistic implementation of these dispersal processes into a simulation model would allow an assessment of the importance of diversity for the migration of plant species in various environments worldwide. To date, dynamic global vegetation models have been optimized for a global application and overestimate the migration of biome shifts in currently warming temperatures. We hypothesize that this is caused by neglecting important fine-scale processes, which are necessary to estimate realistic vegetation trajectories. Recently, we built and parameterized a simulation model LAVESI for larches that dominate the latitudinal treelines in the northernmost areas of Siberia. In this study, we updated the vegetation model by including seed and pollen dispersal driven by wind speed and direction. The seed dispersal is modelled as a ballistic flight, and for the pollination of ovules of seeds produced, we implemented a wind-determined and distance-dependent probability distribution function using a von Mises distribution to select the pollen donor. A local sensitivity analysis of both processes supported the robustness of the model's results to the parameterization, although it highlighted the importance of recruitment and seed dispersal traits for migration rates. This individual-based and spatially explicit implementation of both dispersal processes makes it easily feasible to inherit plant traits and genetic information to assess the impact of migration processes on the genetics. Finally, we suggest how the final model can be applied to substantially help in unveiling the important drivers of migration dynamics and, with this, guide the improvement of recent global vegetation models.

Dispersal distances and migration rates at the arctic treeline in Siberia – a genetic and simulation-based study

“…The updated version with a wind-dependent seed dispersal kernel named LAVESI-WIND is available in the first version, version 1.0, and is accessible at GitHub at https://github. com/StefanKruse/LAVESI/tree/v1.0 (last access: 4 February 2018) and stored at https://doi.org/10.5281/zenodo.1165383 (Kruse et al, 2018c).…”

Treeline Research—From the Roots of the Past to Present Time. A Review

“…Since the 1990s, the modelling of treeline and its environmental constraints has rapidly increased. Modelling, in particular when combined with field-and laboratory experiments, allows alternative scenarios of possible treeline response to environmental pressure ( Figure 6) (e.g., [87,137,138,145,172,173,258,289,350,365,373,409,[420][421][422][423][424][425][426][427][428][429][430][431]). Due to rapidly growing numbers and the great diversity of modelling techniques it may be difficult, however, to find the most appropriate method (e.g., [432][433][434]).…”

LPJ-GM 1.0: simulating migration efficiently in a dynamic vegetation model

“…To also account for ecophysiology, the CATS model was combined with LPJ-GUESS in a post-processing approach (Lehsten et al, 2014). A spatio-temporally explicit suitability for a single species was estimated from LPJ-GUESS simulations of the productivity of this species, assuming the presence of the other species.…”