Methods (see full publication for additional information)
Study area
Berchtesgaden National Park comprises a cool temperate mountain landscape in the Northern Limestone Alps, in the southeastern tip of Germany along the border with Austria (Figure 1). The landscape is rugged and topographically complex, ranging from 603-2,713 m in elevation (Nationalpark Berchtesgaden, 2023). Mean annual temperature decreases (7 to -2 °C) and annual precipitation increases (1,500 to 2,600 mm) with elevation, and precipitation peaks during the summer. Soils are primarily derived from calcareous limestone and dolomite, and shallow to intermediate depth Rendzic soil types and Cambisols cover much of the landscape (Nationalpark Berchtesgaden, 2023; Thom & Seidl, 2022).
The Park is 20,808 ha in size, 44% of which is forested. Due to legacies of intensive timber harvest and replanting since the 1500s, much of today’s forested area is dominated by structurally simple, homogeneous stands of Norway spruce (Picea abies (L.) Karst.). Mixed deciduous forests dominated by European beech (Fagus sylvatica L.) can be found at lower elevations (submontane zone, < 850 m elevation). Silver fir (Abies alba Mill.) and European beech are locally abundant or intermixed with Norway spruce in montane forests (850-1,400 m elevation). Higher elevation (1,400 m to tree line) subalpine forests include open stands of European larch (Larix decidua L.), pockets of Swiss stone pine (Pinus cembra L.), and patches of dwarf mountain pine (Pinus mugo Turra) near upper tree line (~1,800 m; Figure 1a). Management ceased in a 13,860 ha core zone following the creation of the national park in 1978, and over the past few decades forests have become more structurally complex and species rich (Thom & Seidl, 2022). Outside of the core zone, management includes ungulate management (mainly hunting), bark beetle mitigation (bark or tree removal), and forest restoration (planting of tree species to restore natural assemblages). Cattle grazing is restricted to the management zone of the national park and occurs mainly in non-forested areas. Common natural disturbances include windstorms, bark beetle outbreaks, and avalanches, but disturbances tend to be small and affect a relatively low proportion of forested area (average 0.2% of area disturbed per year between 1986 and 2020, median patch size < 1 ha; Maroschek et al., 2023; Senf et al., 2017). Warmer climate, changes in timing and amount of precipitation, increasing disturbance impacts, and continuing recovery from past human land use are all expected to affect mountain forest development trajectories over the 21st century (Albrich et al., 2022; Dollinger et al., 2023; Thom et al., 2022).
Berchtesgaden National Park is situated in a European hotspot of plant species richness (Večeřa et al., 2019). This diversity reflects broad gradients in temperature, topography, and habitat type, coupled with high precipitation. The species pool includes many species characteristic of the northern Alps, but also relatively isolated populations of species mainly distributed in the southern and central Alps that only survived in a few northern locations following previous ice ages. A 2021 survey in Berchtesgaden National Park identified 27 forest understory species listed as threatened and 46 as extremely rare or near threatened on the German Red List.
Simulation model overview and evaluation
We simulated forest change in Berchtesgaden National Park in the absence of future management using the individual-based forest landscape and disturbance model iLand. This process-based model simulates forest structure, functioning, and species composition as an emergent property of individual tree responses to competition, climate and environmental drivers, and disturbance (Seidl et al., 2012; Thom et al., 2022). Competition for light is modeled at 2 m horizontal resolution as a function of incoming radiation and shading by individual tree crowns. Light availability at the forest floor is further attenuated by the forest canopy and varies with height. Tree growth, mortality, and regeneration are dictated by species-specific responses to abiotic drivers such as light, temperature, and carbon dioxide concentration, as well as soil water and nutrient availability. Disturbances are spatially explicit, and effects depend on disturbance intensity, landscape context, species traits, and individual tree characteristics. For example, tree mortality due to wind disturbance varies with stand height, proximity to forest edge, and resistance to uprooting and stem breakage. In iLand, fallen spruce trees may then be colonized by the European spruce bark beetle (Ips typographus L.), which is the most important biotic disturbance agent in Europe (Patacca et al., 2023). Bark beetle spread and outbreak severity depend on temperature, beetle phenology, and the availability and defense of host trees above a size threshold. Full model documentation can be found online at https://iland-model.org.
The iLand model has been widely applied in forested landscapes across Central Europe (e.g., Petter et al., 2020; Thom et al., 2017), North America (e.g., Hansen et al., 2021; Turner et al., 2022), and Japan (Kobayashi et al., 2023). Over 30 Central European tree species have been parameterized, including all major and most minor tree species occurring in Berchtesgaden National Park. In evaluation simulations for Berchtesgaden National Park, iLand successfully reproduced expected productivity by species and stand age in comparison with independent forest inventory data, forest type in comparison with potential natural vegetation maps, and spatial patterns of wind and bark beetle disturbance in comparison with observed data (see Supplementary material in Thom et al., 2022 for detailed evaluations).
Initial conditions and drivers
Spatially contiguous soil and forest conditions were previously derived for the forested area in Berchtesgaden National Park (8,645 ha) by Thom et al. (2022). Soil texture, depth, fertility, and carbon stocks were assigned (1-ha resolution) based on a soil type map (Konnert, 2004) and representative values from local or regional data (Seidl et al., 2009). Forest inventory data from 3,559 regularly spaced plots collected between 2010-2012 was used in combination with a forest type map to initialize stand structure and tree species composition. Forest change was then simulated from 2011 to 2020 (Thom et al., 2022), and spatially explicit disturbances during this period were prescribed using remotely sensed data (Senf et al., 2017). The tree vegetation in the year 2020 served as the starting point for the current analysis.
Daily climate drivers (minimum and maximum temperature, precipitation, vapor pressure deficit, and solar radiation) were derived at 1-ha resolution for both historical (1980-2009) and future (2010-2100) periods (Thom et al., 2022). To estimate spatially explicit historical climate in this topographically complex landscape, outputs from a 5-km spatial and 1-hour temporal resolution dynamic regional climate model for Central Europe (Warscher et al., 2019) were bias corrected with data from 35 weather stations distributed throughout the watershed encompassing the national park and then interpolated to 100-m resolution at a daily timestep. Future regional climate change scenarios at 5-km spatial and daily temporal resolution were acquired from the Bayerisches Landesamt für Umwelt (Zier et al., 2020). Because these projections were coarse relative to the scale of the landscape, average daily climate change was computed for each scenario and used to offset 100-m resolution historical climate data, thus conserving the underlying topographic and temporal variation (see Supplementary material in Thom et al., 2022). For computational efficiency, climate data was further aggregated into 800 clusters characterized by consistent monthly climate values (Thom et al., 2022).
Understory plant community and forest inventory data
Understory plant community data were collected during the 2021 growing season in a balanced sample of 150 forested plots stratified by elevation (50 each from submontane, montane, and subalpine zones) and development stage (10 per elevation zone from gap/regeneration, establishment, optimum, plenter/uneven-aged, and terminal/decay stages; Zenner et al., 2016) to represent the range of forest conditions present across Berchtesgaden National Park (Figure 1a). Understory plants were identified to the species level and overlapping percent cover was recorded visually by species in square 200 m2 plots using the Londo decimal scale (Londo, 1976). Additional information on individual species, including life form (fern, graminoid, herb, or shrub), Ellenberg indicator values (EIVs; Ellenberg et al., 2001; Ellenberg & Leuschner, 2010), and German Red List status were compiled from the TRY Plant Trait Database (Kattge et al., 2011, 2020), Botanical Information Node Bavaria (Arbeitsgemeinschaft Flora von Bayern, 2023), and E.C.O. Institute for Ecology (personal communication, Tobias Köstl). Species were grouped into six Plant Functional Types (PFTs) based on their EIVs for temperature and light (light: light-preferring or shade-tolerant; temperature: warm-preferring, cold-preferring, or indifferent; Table S2).
Forest inventory data and light measurements were collected in the 2021 growing season at the plot locations of the vegetation survey. Individual tree species and diameter at breast height (DBH) were recorded in variable radius subplots based on tree size (within a 500 m2 circular plot all trees ≥ 20 cm DBH were recorded, within 150 m2 trees ≥ 12 cm DBH, within 50 m2 trees ≥ 6 cm DBH, and within 25 m2 trees ≥ 0.2 m height). Light availability (Total Site Factor [TSF]) was measured at plot center and 10 m from plot center in the four cardinal directions with a hemispheric photo taken with a Solariscope SOL 300 (Ing. Behling) two meters above ground. The best threshold separating canopy from sky was independently selected by three interpreters based on visual interpretation and cross-checked for consistency. The most commonly selected threshold was used for each plot, or if there was high deviation (delta TSF ≥ 0.03), a re-evaluation was performed before choosing the final threshold. Light measurements were averaged across the five measurements to represent the average light conditions per plot.
Statistical modeling of understory plant communities
We fit random forest models to predict individual species presence and total understory percent cover as a function of climate, forest, and soil conditions (see Supplementary material for additional details). Understory species included only vascular plants and excluded trees (i.e., only ferns, graminoids, herbs, and shrubs were included), and models did not explicitly consider dispersal limitations. Species names were first reviewed to identify synonymous species, and individual SDMs were only fit for observations identified to the species level and for species that were present in at least five plots. This resulted in SDMs for 248 individual species (Table S2) out of a total of 445 unique understory species recorded in the field. Most species for which we did not fit an SDM (113 of 197 species not included in our models) were present in only one or two plots. Total understory cover was summed across all vascular understory plants, including those that were not modeled with individual SDMs. Percent cover could be greater than 100% because the cover of individual species can overlap.
We selected a set of potential climate, forest, and soil predictors based on drivers of biodiversity and species composition in the European Alps identified in recent studies (Chauvier et al., 2021; Helm et al., 2017; Thom et al., 2017), expectations for ecologically meaningful drivers of plant communities (Gardner et al., 2019; Landuyt et al., 2018), and available data at a comparable spatial resolution (Table S1; see also best practices outlined in Araújo et al., 2019). Climate and soil predictors were derived from the same datasets used to drive iLand simulations based on the location of plot centroids, with climate variables calculated as decadal averages from the most recent historical climate data (2000-2009). Forest predictors were derived from field data and included light availability. We identified a balanced set of three climate, three forest, and three soil variables to include as final model predictors based on a variable selection process. First, we identified highly correlated predictors (Pearson’s |r| > 0.7); this included most climate predictors. Second, for forest predictors only, we fit initial random forest SDMs and identified the most important structure and composition predictors based on percent increase in mean-squared-error (%IncMSE; Figure S1). Final predictors (all pairwise |r| < 0.7) were selected based on a priori expectations about causal relationships and to provide contrasting predictive information within each category. Selected predictors were mean annual temperature (°C), summer precipitation sum (mm), and mean annual global radiation (MJ m2 day-1; climate); relative light availability at the forest floor (0-1), basal area (m2 ha-1), and proportion beech (0-1; forest); and percent sand (%), water holding capacity (mm), and soil fertility (kg available N ha-1; soil). All predictors were z-score standardized to have a mean of 0 and standard deviation of 1; if no trees were present, proportion beech was set to 0 (i.e., the mean value) after standardization.
To simultaneously evaluate individual species SDMs and plot-level predictions, we performed repeated subsampling into 70% training and 30% test data (n = 20 subsamples, which ensured that test datasets included predictions for each individual species in each plot). Random forest models were fit using the randomForest package (Liaw & Wiener, 2002) in R 4.1.3 (R Core Team, 2022) with 1000 trees, node size of five, three predictors per split (number of predictors/3; Breiman, 2001), and weighted sampling of presences and absences to account for species prevalence (i.e., summed weight of presences = summed weight of absences). Individual SDMs were evaluated based on area under the receiving operator characteristic curve (AUC). We then stacked individual SDMs to derive community-level predictors of species richness and temperature EIV. Final species richness predictions were bias corrected to account for overestimation of richness due to weighted sampling, as well as overestimation of richness at low values and underestimation at high values. Specifically, we modeled differences between observed and predicted richness in the training data set and used parameters estimated from these models to adjust predicted richness (for detailed methods, see Calabrese et al., 2014; Zurell et al., 2020). To estimate community-level temperature EIV, we used probability ranking to determine the most likely species present in the community up to total richness (D’Amen et al. 2015) and calculated average temperature EIV across these species. Separate random forest models were fit to predict total understory percent cover. Richness, temperature EIV, and percent cover were evaluated based on goodness-of-fit (R2) for test dataset predictions. Partial plots for each predictor were also evaluated to ensure they aligned with ecological expectations. Variable importance was assessed with %IncMSE.
Final models were fit to the full dataset. Models for species presence, bias corrected richness, temperature EIV, and percent cover were used to predict contemporary understory plant species communities at 10 m resolution across all forested areas in Berchtesgaden National Park (n = 864,466 grid cells). Climate, soil, and forest predictors were consistent with the ones used as input for iLand. Field plots were generally representative of environmental and forest conditions across the full landscape (Figure S3). All predictors except light availability were rescaled to match standardized field data predictor values. Light availability derived from iLand is similar but not identical to field-measured TSF, so light availability was z-score standardized assuming field plots covered the range of light conditions present in the landscape.
Individual SDM fits varied among species (Table S2). We evaluated whether the inclusion of species with poorer model fits affected our overall results by generating a second set of predictions including only species with AUC > 0.7 (n = 174 species). All analyses were also re-run with species richness and temperature EIV derived from this subset of better-performing models.
Simulation scenarios
We simulated a full factorial combination of two representative concentration pathways (RCPs), two general circulation models (GCMs), and two future disturbance scenarios (n = 8 total scenarios, 2 RCP x 2 GCM x 2 disturbance) based on contrasts and key uncertainties in future change for this region (Zier et al., 2020). RCPs included RCP4.5 (warmer climate) and RCP8.5 (hotter climate, which most closely tracks current carbon emissions trajectories; Schwalm et al., 2020), with the respective changes in atmospheric CO2 concentrations considered in the forest simulations with iLand. GCMs were selected to include wetter (ICHEC-EC-EARTH) and drier (MPI-M-MPI-ESM-LR) scenarios (Zier et al., 2020). Mean annual temperature change between historical and late 21st-century (2091-2100) periods averaged +2.2 °C (RCP4.5) and +5.1 °C (RCP8.5), and summer precipitation either increased by 56 mm (ICHEC-EC-EARTH) or decreased by 109 mm (MPI-M-MPI-ESM-LR). Two wind disturbance scenarios were simulated, including baseline wind (“baseline disturbance”), in which historical wind frequency, timing, speed, and direction from 14 local weather stations were used to project future scenarios (Thom et al., 2022), and a uniform 15% increase in wind speed (“high disturbance”; Albrich et al., 2022), which is at the upper range of projected changes in wind speed in this region (Fink et al., 2009). Future forest change was simulated from initial conditions in 2020 until 2100 (n = 10 replicates per scenario), and simulations also included dynamic bark beetle disturbances. We did not include future forest management or browsing in our simulations because we expect future management to be limited in this landscape.
21st-century change in understory plant communities (Q1)
For each future simulation scenario, understory plant communities were predicted at 10 m resolution in year 2050 (near-term change) and year 2100 (long-term change). Future climate predictors were the averages of the preceding decade (e.g., 2041-2050 for 2050), and forest predictors were derived from simulated forest structure, composition, and light availability in the given year (e.g., 2050 for 2050). Richness, temperature EIV as an indicator of thermophilization, and total understory cover were averaged for each replicate (n = 80; 2 RCPs x 2 GCMs x 2 disturbance scenarios x 10 replicates) to analyze overall landscape trends.
To assess patterns and drivers of fine-scale change, we used Spearman’s rank correlations and pairwise plots to evaluate relationships among response variables (richness, thermophilization, and cover), drivers, and elevation. We explored whether changes were consistent among responses, whether fine-scale changes in forest structure resulted in expected changes in cover and alpha diversity, and whether changes varied across the elevational gradient. To evaluate shifts in plant community composition, temporal species turnover was calculated for each cell (Cleland et al., 2013):
(Number of new species+number of lost species in future climate year relative to 2020)/(Total number of species in either 2020 or future climate year) x 100%
We quantified gamma diversity for all species and by Red List category based on the number of species present anywhere in the full landscape. Species were further grouped by PFT or life form to examine temporal changes in group dominance across the elevational gradient and identify potential winners and losers under future change.
Importance of climate versus forest change for future understory community change (Q2)
A random sample of 1,000 10-m cells (minimum distance between samples = 100 m) was used to analyze the importance of climate versus forest change in driving understory change, while also considering the effect of local context. This sample represented the range of conditions in drivers and responses across the landscape (Figure S4). We predicted understory species richness, temperature EIV, and percent cover in each sampled cell under two climate levels (contemporary, future) and three forest levels (contemporary, baseline disturbance, high disturbance), using all four combinations of RCPs x GCMs. Because contemporary climate and contemporary forest conditions do not vary by RCP or GCM, this resulted in 21 total combinations (Table S3). For each future replicate, separate linear mixed effects models were fit explaining understory communities in 2050 and 2100 from forest change, climate change, and their interactions as fixed effects and sample cell number as a random effect (to account for variability due to local context). To address unequal variance among groups, a separate variance parameter was estimated for each fixed effect group using the nlme package in R (Pinheiro et al., 2022). Species richness was 1/square-root transformed and percent cover was square-root transformed to improve residual distributions. Some model residuals exhibited longer tails relative to normal distributions, but we concluded that model results were robust based on quantile-quantile plots and relative changes in group means (Pinheiro & Bates, 2000). Overall model fit and the relative contribution of fixed versus random effects was assessed with marginal and conditional R2 (Nakagawa & Schielzeth, 2013). We quantified the relative and shared importance of drivers by fitting separate models for each fixed effect and calculating their contribution to marginal R2.
Literature cited
Albrich, K., Seidl, R., Rammer, W., & Thom, D. (2022). From sink to source: Changing climate and disturbance regimes could tip the 21st century carbon balance of an unmanaged mountain forest landscape. Forestry: An International Journal of Forest Research, 95(5), 742. https://doi.org/10.1093/forestry/cpac033
Araújo, M. B., Anderson, R. P., Barbosa, A. M., Beale, C. M., Dormann, C. F., Early, R., Garcia, R. A., Guisan, A., Maiorano, L., Naimi, B., O’Hara, R. B., Zimmermann, N. E., & Rahbek, C. (2019). Standards for distribution models in biodiversity assessments. Science Advances, 5(1), eaat4858. https://doi.org/10.1126/sciadv.aat4858
Arbeitsgemeinschaft Flora von Bayern. (2023). Botanischer Informationsknoten Bayern. Staatliche Naturwissenschaftliche Sammlungen Bayerns. http://daten.bayernflora.de
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
Calabrese, J. M., Certain, G., Kraan, C., & Dormann, C. F. (2014). Stacking species distribution models and adjusting bias by linking them to macroecological models. Global Ecology and Biogeography, 23(1), 99–112. https://doi.org/10.1111/geb.12102
Chauvier, Y., Thuiller, W., Brun, P., Lavergne, S., Descombes, P., Karger, D. N., Renaud, J., & Zimmermann, N. E. (2021). Influence of climate, soil, and land cover on plant species distribution in the European Alps. Ecological Monographs, 91(2), e01433. https://doi.org/10.1002/ecm.1433
Cleland, E. E., Collins, S. L., Dickson, T. L., Farrer, E. C., Gross, K. L., Gherardi, L. A., Hallett, L. M., Hobbs, R. J., Hsu, J. S., Turnbull, L., & Suding, K. N. (2013). Sensitivity of grassland plant community composition to spatial vs. Temporal variation in precipitation. Ecology, 94(8), 1687–1696. https://doi.org/10.1890/12-1006.1
Dollinger, C., Rammer, W., & Seidl, R. (2023). Climate change accelerates ecosystem restoration in the mountain forests of Central Europe. Journal of Applied Ecology, n/a(n/a). https://doi.org/10.1111/1365-2664.14520
Ellenberg, H., Düll, R., Wirth, V., Werner, W., & Paulißen, D. (2001). Zeigerwerte von Pflanzen in Mitteleuropa (3rd ed.). Scripta Geobotanica XVIII. Verlag Erich Goltze GmbH & Co. KG.
Ellenberg, H., & Leuschner, C. (2010). Vegetation Mitteleuropas mit den Alpen. In ökologischer, dynamischer und historischer Sicht (6th ed.). UTB.
Fink, A. H., Brücher, T., Ermert, V., Krüger, A., & Pinto, J. G. (2009). The European storm Kyrill in January 2007: Synoptic evolution, meteorological impacts and some considerations with respect to climate change. Natural Hazards and Earth System Science, 9(2), 405–423. https://doi.org/10.5194/nhess-9-405-2009
Gardner, A. S., Maclean, I. M. D., & Gaston, K. J. (2019). Climatic predictors of species distributions neglect biophysiologically meaningful variables. Diversity and Distributions, 25(8), 1318–1333. https://doi.org/10.1111/ddi.12939
Hansen, W. D., Fitzsimmons, R., Olnes, J., & Williams, A. P. (2021). An alternate vegetation type proves resilient and persists for decades following forest conversion in the North American boreal biome. Journal of Ecology, 109(1), 85–98. https://doi.org/10.1111/1365-2745.13446
Helm, N., Essl, F., Mirtl, M., & Dirnböck, T. (2017). Multiple environmental changes drive forest floor vegetation in a temperate mountain forest. Ecology and Evolution, 7(7), 2155–2168. https://doi.org/10.1002/ece3.2801
Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Tautenhahn, S., Werner, G. D. A., Aakala, T., Abedi, M., Acosta, A. T. R., Adamidis, G. C., Adamson, K., Aiba, M., Albert, C. H., Alcántara, J. M., C, C. A., Aleixo, I., Ali, H., … Wirth, C. (2020). TRY plant trait database – enhanced coverage and open access. Global Change Biology, 26(1), 119–188. https://doi.org/10.1111/gcb.14904
Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., Bodegom, P. M. V., Reichstein, M., Enquist, B. J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., … Wirth, C. (2011). TRY - a global database of plant traits. Global Change Biology, 17(9), 2905–2935. https://doi.org/10.1111/j.1365-2486.2011.02451.x
Kobayashi, Y., Seidl, R., Rammer, W., Suzuki, K. F., & Mori, A. S. (2023). Identifying effective tree planting schemes to restore forest carbon and biodiversity in Shiretoko National Park, Japan. Restoration Ecology, 31(1), e13681.
Konnert, V. (2004). Standortkarte Nationalpark Berchtesgaden. Nationalpark Berchtesgaden, Forschungsbericht 49.
Landuyt, D., Perring, M. P., Seidl, R., Taubert, F., Verbeeck, H., & Verheyen, K. (2018). Modelling understorey dynamics in temperate forests under global change–Challenges and perspectives. Perspectives in Plant Ecology, Evolution and Systematics, 31, 44–54. https://doi.org/10.1016/j.ppees.2018.01.002
Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18–22.
Londo, G. (1976). The decimal scale for releves of permanent quadrats. Vegetatio, 33(1), 61–64. https://doi.org/10.1007/BF00055300
Maroschek, M., Seidl, R., Poschlod, B., & Senf, C. (2023). Quantifying patch size distributions of forest disturbances in protected areas across the European Alps. Journal of Biogeography, n/a(n/a). https://doi.org/10.1111/jbi.14760
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x
Nationalpark Berchtesgaden. (2023). Nationalparkplan 2023-2033: Bestandesplan—Grundlagen und Analysen. Bayerisches Staatsministerium für Landesentwicklung und Umweltfragen.
Patacca, M., Lindner, M., Lucas-Borja, M. E., Cordonnier, T., Fidej, G., Gardiner, B., Hauf, Y., Jasinevičius, G., Labonne, S., Linkevičius, E., Mahnken, M., Milanovic, S., Nabuurs, G.-J., Nagel, T. A., Nikinmaa, L., Panyatov, M., Bercak, R., Seidl, R., Ostrogović Sever, M. Z., … Schelhaas, M.-J. (2023). Significant increase in natural disturbance impacts on European forests since 1950. Global Change Biology, 29(5), 1359–1376. https://doi.org/10.1111/gcb.16531
Petter, G., Mairota, P., Albrich, K., Bebi, P., Brůna, J., Bugmann, H., Haffenden, A., Scheller, R. M., Schmatz, D. R., Seidl, R., Speich, M., Vacchiano, G., & Lischke, H. (2020). How robust are future projections of forest landscape dynamics? Insights from a systematic comparison of four forest landscape models. Environmental Modelling and Software, 134, 104844. https://doi.org/10.1016/j.envsoft.2020.104844
Pinheiro, J. C., & Bates, D. M. (2000). Mixed-Effects Models in S and S-Plus: Statistics and Computing. In Mixed-Effects Models in S and S-PLUS. Springer-Verlag.
Pinheiro, J. C., Bates, D. M., DebRoy, S., Sarkar, D., & R Core Team. (2022). nlme: Linear and Nonlinear Mixed Effects Models [Computer software]. R package version 3.1-155. https://CRAN.R-project.org/package=nlme.
R Core Team. (2022). R: A language and environment for statistical computing.
Schwalm, C. R., Glendon, S., & Duffy, P. B. (2020). RCP8.5 tracks cumulative CO2 emissions. Proceedings of the National Academy of Sciences of the United States of America, 117(33), 19656–19657. https://doi.org/10.1073/PNAS.2007117117
Seidl, R., Rammer, W., & Lexer, M. J. (2009). Schätzung von Bodenmerkmalen und Modellparametern für die Waldökosystemsimulation auf Basis einer Großrauminventur. Allgemeine Forst- Und Jagdzeitung, 180(1–2), 35–44.
Seidl, R., Rammer, W., Scheller, R. M., & Spies, T. A. (2012). An individual-based process model to simulate landscape-scale forest ecosystem dynamics. Ecological Modelling, 231, 87–100. https://doi.org/10.1016/j.ecolmodel.2012.02.015
Senf, C., Pflugmacher, D., Hostert, P., & Seidl, R. (2017). Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 453–463. https://doi.org/10.1016/j.isprsjprs.2017.07.004
Thom, D., Rammer, W., Dirnböck, T., Müller, J., Kobler, J., Katzensteiner, K., Helm, N., & Seidl, R. (2017). The impacts of climate change and disturbance on spatio-temporal trajectories of biodiversity in a temperate forest landscape. Journal of Applied Ecology, 54(1), 28–38. https://doi.org/10.1111/1365-2664.12644
Thom, D., Rammer, W., Laux, P., Smiatek, G., Kunstmann, H., Seibold, S., & Seidl, R. (2022). Will forest dynamics continue to accelerate throughout the 21st century in the Northern Alps? Global Change Biology, 28(10), 3260–3274. https://doi.org/10.1111/gcb.16133
Thom, D., & Seidl, R. (2022). Accelerating mountain forest dynamics in the Alps. Ecosystems, 25(3), 603–617. https://doi.org/10.1007/s10021-021-00674-0
Turner, M. G., Braziunas, K. H., Hansen, W. D., Hoecker, T. J., Rammer, W., Ratajczak, Z., Westerling, A. L., & Seidl, R. (2022). The magnitude, direction, and tempo of forest change in Greater Yellowstone in a warmer world with more fire. Ecological Monographs, e01485. https://doi.org/10.1002/ecm.1485
Večeřa, M., Divíšek, J., Lenoir, J., Jiménez-Alfaro, B., Biurrun, I., Knollová, I., Agrillo, E., Campos, J. A., Čarni, A., Jiménez, G. C., Ćuk, M., Dimopoulos, P., Ewald, J., Fernández-González, F., Gégout, J. C., Indreica, A., Jandt, U., Jansen, F., Kącki, Z., … Chytrý, M. (2019). Alpha diversity of vascular plants in European forests. Journal of Biogeography, 46(9), 1919–1935. https://doi.org/10.1111/jbi.13624
Warscher, M., Wagner, S., Marke, T., Laux, P., Smiatek, G., Strasser, U., & Kunstmann, H. (2019). A 5 km resolution regional climate simulation for Central Europe: Performance in high mountain areas and seasonal, regional and elevation-dependent variations. Atmosphere, 10(11), 682. https://doi.org/10.3390/atmos10110682
Zenner, E. K., Peck, J. L. E., Hobi, M. L., & Commarmot, B. (2016). Validation of a classification protocol: Meeting the prospect requirement and ensuring distinctiveness when assigning forest development phases. Applied Vegetation Science, 19(3), 541–552. https://doi.org/10.1111/avsc.12231
Zier, C., Müller, C., Komischke, H., Steinbauer, A., & Bäse, F. (2020). Das Bayerische Klimaprojektionsensemble—Audit und Ensemblebildung (pp. 1–52). Bayerisches Landesamt für Umwelt (LfU).
Zurell, D., Zimmermann, N. E., Gross, H., Baltensweiler, A., Sattler, T., & Wüest, R. O. (2020). Testing species assemblage predictions from stacked and joint species distribution models. Journal of Biogeography, 47(1), 101–113. https://doi.org/10.1111/jbi.13608
