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MS-M was supported by the Spanish Ministry of Science and Innovation (FPU predoctoral grant and the project PROPIFEN PGC2018-101773-B-I00). MM acknowledges a grant from the Swiss National Science Foundation (project n.175101) and funding from the Canada Research Chairs Program. PS was funded by the project "Ecologia Funcional de los Bosques Andaluces y Predicciones Sobre Sus Cambios Futuros" (ForChange) (UCO-27943) by Junta de Andalucia (Spain), the project "Funcionalidad y servicios ecosistemicos de los bosques andaluces y normarroquies: relaciones con la diversidad vegetal y edafica ante el cambio climatico" by "Ayudas a la I CD del Plan Andaluz de Investigacion, Desarrollo e Innovacion (PAIDI) 2020," Junta de Andalucia (Spain) and European FEDER funds. JQ and RN-C were funded by the project ESPECTRAMED (CGL2018-86161-R) from Spanish Research Agency, Ministry of Science and Innovation. RN-C was funded by the projects ISOPINE (UCO-1265298) and SilvAdpat Network RED2018-102719., We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu). We thank Robert Scheller, Brian Miranda, Rafael Villar, and Núria Aquilué for useful insights on project conceptualization during the initial steps of this work. We also thank two reviewers for the comments and suggestions made to earlier versions of this manuscript., The use of spatially interactive forest landscape models has increased in recent years. These models are valuable tools to assess our knowledge about the functioning and provisioning of ecosystems as well as essential allies when predicting future changes. However, developing the necessary inputs and preparing them for research studies require substantial initial investments in terms of time. Although model initialization and calibration often take the largest amount of modelers' efforts, such processes are rarely reported thoroughly in application studies. Our study documents the process of calibrating and setting up an ecophysiologically based forest landscape model (LANDIS-II with PnET-Succession) in a biogeographical region where such a model has never been applied to date (southwestern Mediterranean mountains in Europe). We describe the methodological process necessary to produce the required spatial inputs expressing initial vegetation and site conditions. We test model behaviour on single-cell simulations and calibrate species parameters using local biomass estimations and literature information. Finally, we test how different initialization data-with and without shrub communities-influence the simulation of forest dynamics by applying the calibrated model at landscape level. Combination of plot-level data with vegetation maps allowed us to generate a detailed map of initial tree and shrub communities. Single-cell simulations revealed that the model was able to reproduce realistic biomass estimates and competitive effects for different forest types included in the landscape, as well as plausible monthly growth patterns of species growing in Mediterranean mountains. Our results highlight the importance of considering shrub communities in forest landscape models, as they influence the temporal dynamics of tree species. Besides, our results show that, in the absence of natural disturbances, harvesting or climate change, landscape-level simulations projected a general increase of biomass of several species over the next decades but with distinct spatio-temporal patterns due to competitive effects and landscape heterogeneity. Providing a step-by-step workflow to initialize and calibrate a forest landscape model, our study encourages new users to use such tools in forestry and climate change applications. Thus, we advocate for documenting initialization processes in a transparent and reproducible manner in forest landscape modelling., Spanish Government PROPIFEN PGC2018-101773-B-I00, Swiss National Science Foundation (SNSF) European Commission 175101, Canada Research Chairs, Junta de Andalucia European Commission UCO-27943, project "Funcionalidad y servicios ecosistemicos de los bosques andaluces y normarroquies: relaciones con la diversidad vegetal y edafica ante el cambio climatico" by "Ayudas a la I CD del Plan Andaluz de Investigacion, Desarrollo e Innovacion (PAIDI) 2020, European Commission, project ESPECTRAMED from Spanish Research Agency, Ministry of Science and Innovation CGL2018-86161-R, project ISOPINE UCO-1265298, Project SilvAdpat Network RED2018-102719, Spanish Ministry of Science and Innovation (FPU predoctoral grant)

Climate change has been observed to significantly influence forest growth, community composition, and species distribution ranges. These influences in turn will impose continuous impacts on forest production and carbon (C) storage potential. Forests in the subtropical China that are experiencing rapid regeneration and recovery may suffer multiple threats in the face of future climate change. Understanding how climate change may affect forest C sequestration and species dynamics over time will help formulate better management strategies for maintaining forest productivity and biodiversity. Here, we used a forest landscape model (LANDIS‐II) to evaluate the long‐term effects of current business‐as‐usual (BAU) management and climate projections (current, RCP4.5, and RCP8.5 climate scenarios; IPCC representative concentration pathways [RCPs] scenarios) on above‐ and belowground forest C storage and tree species dynamics in the Sangzhi County in the subtropical China. Our simulations showed a fast‐growing period of forest total C in the first 70 yr, regardless of climate regime. Moderate climate change (RCP4.5 climate scenario) increased soil organic carbon (SOC) (12%) and detrital C (16%) but reduced live C (5%), contributing to a slight augment of 3% in forest C storage compared to the control climate, while severe climate change (RCP8.5 climate scenario) decreased SOC (16%), detrital C (27%), and live C (12%), resulting in a dramatic reduction of 14% in forest C storage, primarily because severe warming‐induced water stress restrained species establishment and regeneration in temperature‐sensitive areas like the lower elevations. Meanwhile, nature reserves in the higher elevations could act as “safe islands” by providing suitable conditions for most tree species, but the logging ban caused higher canopy closure, which in turn inhibit the growth and establishment of shade‐intolerant species. The results also highlighted the positive responses of native “warm species” to climate warming and suggest that using them to replace some conventional coniferous plantation tree species would better mitigate the future climate change. Poor performance of the current BAU management in maintaining forest productivity and diversity suggests that new climate‐adapted management strategies should be designed accordingly.

Fire and timber harvest are two major forest disturbances in boreal forests. Predicting the dynamics of boreal forest biomass requires accounting for both of those effects. Related stochasticity and other uncertainties can produce great variation in predicted responses of forests to fire and timber harvest. In this study, we investigated the effects of fire and timber harvest on landscape-level predictions of the tree component of stand biomass in a boreal forest landscape in Northeast China. We used a forest landscape model (LANDIS PRO) to predict the tree biomass over three time intervals (0–50, 50–150, and 150–300 years). We then compared the simulated results of fire and timber harvest and their interactions with observed biomass and its spatial distribution over short-, mid-, and long-term intervals. For additional prediction comparisons, we observed uncut, unburned stands (i.e., the succession-only scenario). Compared to the succession-only scenario, we found that predicted biomass was reduced by 3.8 ± 2.1, 9.1 ± 3.6, and 11.2 ± 5.1 tons/ha in fire-only, harvest-only, and combined fire and harvest scenarios, respectively. Our results indicated that the effect of harvest on biomass exceeded that of fire, and that the interaction of fire and harvest was more effective in reducing biomass than the effects of fire or harvest separately. Biomass predictions that did not consider effects of fire and timber harvest tended to inflate biomass estimates. The spatial distribution of tree biomass moreover changed with simulation period. These results have important implications in designing prescriptions for improving forest sustainability.

LANDIS is a cell-based spatially explicit forest landscape model designed to explore successional dynamics under natural and anthropogenic disturbances. Species age cohort (10-year cohort of a given tree species) information is required for each cell in LANDIS. However, providing such information for a landscape comprising millions of cells is challenging. In this study, a stand-based assignation (SBA) approach was developed to stochastically assign species age cohorts to each cell based on forest inventory data. As a probability-based approach, SBA will introduce errors in LANDIS input. In order to assess the effect of errors produced by SBA on LANDIS results, 20 Monte Carlo simulations were produced. For each species simulated in LANDIS, the recurrence frequency (RF) of the majority species age cohort (MSAC, the most frequently occurring species age cohort) from 20 Monte Carlo simulations were used to quantify the uncertainty in species age cohorts for individual cell. Average recurrence frequency (ARF) of the MSAC was used to quantify the overall uncertainty in species age cohorts at the cell level. For each species, the coefficient of variation (CV) for the percent area and an aggregation index for the 20 Monte Carlo simulations was used to quantify the uncertainty at the landscape level. Results showed that at the cell level, uncertainty was relatively low at the beginning of the simulation (ARF was larger than 10). Seed dispersal, seedling establishment, mortality, and fire disturbance caused uncertainty to increase with simulation year. The uncertainty finally reached an equilibrium state, where input errors in original species age cohorts had little effect on the simulation outcomes. At the landscape level, species percent area and their spatial patterns were not substantially affected by the uncertainties in species age structure at the cell level. Since the typical use of LANDIS is to predict the long-term landscape pattern change, SBA can be used to parameterize species age cohorts for individual cells.

Over the last 20 years, we have seen a rapid development in the field of forest landscape modeling, fueled by both technological and theoretical advances. Two fundamental challenges have persisted since the inception of FLMs: (1) balancing realistic simulation of ecological processes at broad spatial and temporal scales with computing capacity, and (2) validating modeled results using independent, spatially explicit time series data. The paper discusses the current status and future directions regarding these two challenges.

LANDIS is a forest landscape model that simulates the interaction of large landscape processes and forest successional dynamics at tree species level. We discuss how object-oriented design (OOD) approaches such as modularity, abstraction and encapsulation are integrated into the design of LANDIS. We show that using OOD approaches, model decisions (olden as model assumptions) can be made at three levels parallel to our understanding of ecological processes. These decisions can be updated with relative efficiency because OOD components are less interdependent than those designed with traditional approaches. To further examine object design, we examined how forest species objects, AGELIST (tree age-classes), SPECIE (single species) and SPECIES (species list), are designed, linked and functioned. We also discuss in detail the data structure of AGELIST and show that different data structures can significantly affect model performance and model application scopes. Following the discussion of forest species objects, we apply the model to a real forest landscape in northern Wisconsin. We demonstrate the model's capability of tracking species age cohorts in a spatially explicit manner at each time step. The use of these models at large spatial and temporal scales reveals important information that is essential for the management of forested ecosystem.

This chapter reviews the LANDIS and other forest landscape models for integration with wildlife models. LANDIS is a vital-attribute, raster-based forest landscape model. Each raster unit or cell tracks the presence or absence of one to ten year age cohorts of individual species; fuel accumulation level; the time since last disturbance and the time since last management; and species establishment ability in particular land types. For each cell, species birth, growth, death, regeneration, random mortality, and vegetative reproduction are simulated at one to ten year time steps. In LANDIS, vegetation heterogeneity is modeled at multiple hierarchical levels from the landscape to the pixel. Land type captures the highest level (coarse grain) of spatial vegetation-heterogeneity caused by various environmental controls. A somewhat uniform suite of ecological conditions that results in similar species establishment patterns is assumed for each land type. To simulate different types of disturbances and their effects on tree species composition and landscape pattern is central to the design of LANDIS. LANDIS can simulate three different types of natural disturbances (fire, wind, and biological), which can be applied in any combination.