Bayesian calibration of a growth-dependent tree mortality model to simulate the dynamics of European temperate forests

Fitting individual-based models of spatial population dynamics to long-term monitoring data

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

An evaluation of multi-species empirical tree mortality algorithms for dynamic vegetation modelling

Tree mortality is key for projecting forest dynamics, but difficult to portray in dynamic vegetation models (DVMs). Empirical mortality algorithms (MAs) are often considered promising, but little is known about DVM robustness when employing MAs of various structures and origins for multiple species. We analysed empirical MAs for a suite of European tree species within a consistent DVM framework under present and future climates in two climatically different study areas in Switzerland and evaluated their performance using empirical data from old-growth forests across Europe. DVM projections under present climate showed substantial variations when using alternative empirical MAs for the same species. Under climate change, DVM projections showed partly contrasting mortality responses for the same species. These opposing patterns were associated with MA structures (i.e. explanatory variables) and occurred independent of species ecological characteristics. When comparing simulated forest structure with data from old-growth forests, we found frequent overestimations of basal area, which can lead to flawed projections of carbon sequestration and other ecosystem services. While using empirical MAs in DVMs may appear promising, our results emphasize the importance of selecting them cautiously. We therefore synthesize our insights into a guideline for the appropriate use of empirical MAs in DVM applications.

Assessing the response of forest productivity to climate extremes in Switzerland using model-data fusion

The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long-term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960-2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha-1  year-1  km-1 for P. abies and 0.93 ± 0.010 Mg C ha-1  year-1  km-1 for F. sylvatica). During warm-dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm-dry extremes. Importantly, cold-dry extremes had negative impacts on regional forest NPP comparable to warm-dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.