Model Of Forest Dynamics Research Articles

Modelling of forest carbon dynamics in different forest management scenarios: A case study on poplar and black locust plantations in Hungary

Abstract Long-term forest carbon modelling is helpful in climate change mitigation actions. Estimating potential carbon sequestration in forests can be considered the long-term strategy for low carbon and climate resilience in the National Determination Contribution. In Hungary, the black locust (Robinia pseudoacacia) and poplars (Populus sp.) are prominent and dominant species in reforestation and afforestation projects. The research aimed to estimate the carbon dynamics of black locust and white poplar long rotation plantations in some forest management scenarios. Thirty-six forest management scenarios were developed from two species, six yield classes, and tree wood utilization. CO2FIX modelling projected carbon dynamics for 45 years of rotation. Our findings have shown that class yield I resulted in the highest carbon stock compared to class yields II–VI. Black locust plantations have stored carbon to a larger extent than white poplar plantations. In terms of wood utilization, harvested black locust or white poplar wood contributed the most extensive carbon stock if used for pulp. In conclusion, from 36 forest management scenarios, the best scenario was black locust plantation in class yield I and the aim for pulp that stored the carbon at the end of rotation was 101.75 Mg C/ha. In all forest management scenarios, the soil carbon stock was higher than carbon in biomass, products, and bioenergy. Thus, conserving the soil as the dominant carbon pool is vital for future policy recommendations.

A perspective on the need for integrated frameworks linking species distribution and dynamic forest landscape models across spatial scales

Climate change significantly alters species distributions. Numerous studies project the future distribution of species using Species Distribution models (SDMs), most often using coarse resolutions. Working at coarse resolutions in forest ecosystems fails to capture landscape-level dynamics, spatially explicit processes, and temporally defined events that act at finer resolutions and that can disproportionately affect future outcomes. Dynamic Forest Landscape Models (FLMs) can simulate the survival, growth, and mortality of (stands of) trees over long time periods at small resolutions. However, as they are able to simulate at fine resolutions, study landscapes remain relatively small due to computational constraints. The large amount of feedbacks between biodiversity, forest, and ecosystem processes cannot completely be captured by FLMs or SDMs alone. Integrating SDMs with FLMs enables a more detailed understanding of the impact of perturbations on forest landscapes and their biodiversity. Several studies have used this approach at landscape scales, using fine resolutions. Yet, many scientific questions in the fields of biogeography, macroecology, conservation management, among others, require a focus on both large scales and fine resolutions. Here, drawn from literature and experience, we provide our perspective on the most important challenges that need to be overcome to use integrated frameworks at spatial scales larger than the landscape and at fine resolutions. Future research should prioritize these challenges to better understand drivers of species distributions in forest ecosystems and effectively design conservation strategies under the influence of changing climates on spatially and temporally explicit processes. We further discuss possibilities to address these challenges.

Computational assessment of Amazon forest plots regrowth capacity under strong spatial variability for simulating logging scenarios

In this paper, we assess the regrowth capacity of tropical forest plots by developing an original computational procedure based on statistical model checking methods. We calibrate a new mathematical model of forest dynamics with respect to post-logging data, produced in the Amazon basin. Our mathematical model is determined by a mechanistic system of ordinary differential equations and integrates a new nonlinear aging term, which is necessary to reproduce the complex post-logging dynamics of the tropical forest plots. Our method is based on an efficient algorithmic procedure that explores the parameter space of the model and computes a score for each parameter value, depending on the distance between the trajectory of the model and the data. We distinguish a group of four reference plots, for which the logging was the most intense, from another group of five non-reference plots, which are fitted a posteriori. Our results provide a set trajectories of the new model, which successfully fit the non-monotone post-logging data on each forest plot, in spite of the high level of biological variability identified in the study site. Rather than a unique set of parameters, we return a small cell of parameters, extracted from the parameter space, which contains in its close vicinity several relevant sets of parameters that are equally able to reproduce the regrowth dynamics of distinct tropical forest plots. The size of the cells that should be extracted from the parameter space increases with the level of biological variability. Finally, we show how to use the calibrated mathematical model for simulating logging scenarios, so as to better understand the temporal dynamics of forest regrowth.

Innovative educational and research center for monitoring forest resources of Siberia based on laser and microwave aerospace imaging

The possibilities of creating an innovative educational and scientific center for monitoring forest resources in Siberia on the basis of the Department of Space Facilities and Technologies of the Siberian State University of Science and Technology named after Mikhail Fedorovich Reshetnev are discussed, with the aim of training highly qualified engineering personnel and conducting promising scientific research in the field of monitoring, modeling, forecasting and management of forest resources. Methodological solutions and algorithms for three-dimensional modeling of forest structure and dynamics based on laser scanning data, digital aerial and space photography are proposed. These methods contribute to operational monitoring and can significantly reduce the cost of monitoring the condition and use of forest resources over the vast territory of Siberia. Remote sensing data is presented in the form of a geotransformed database and digital photo map, compatible in formats with computer-aided design systems and with the main geographic information systems – ArcView, ArcINFO, MapINFO. The innovative monitoring center will be used for operational state control and monitoring of forest management, the state of forest lands, forest management and forest inventory, solving problems of ecology and environmental management, geoecology, formation of a forest resource inventory, aerospace methods for studying natural resources and territories, information technology. Solving these problems will allow for the training of highly qualified specialists. The center's specialists plan to create information technologies for remote sensing of natural objects with the aim of import substitution of foreign software products. The main scientific directions of the created center: development and research of methods for system analysis of large-scale multidimensional remote sensing data based on nonparametric decision-making algorithms and parallel computing technologies; testing hypotheses about the distributions of large-volume remote sensing data based on nonparametric nuclear-type pattern recognition algorithms; detection of compact groups of large-volume remote sensing data corresponding to unimodal fragments of the joint probability density of multivariate random variables.

Internal response of vegetation growth to degrees of permafrost degradation in Northeast China from 2001 to 2020

ABSTRACT Ecosystems at the southern edge of the permafrost distribution are highly sensitive to global warming. Changes in soil freeze-thaw cycles can influence vegetation growth in permafrost regions. Extant studies mainly focused on analyzing the differences of vegetation dynamics in different permafrost regions. However, the intrinsic drivers of permafrost degradation on vegetation growth remain elusive yet. Based on the top temperature of permafrost (TTOP) model, we simulated the spatial distribution of permafrost in Northeast China (NEC) from 2001 to 2020. Using the data of the vegetation Net Primary Productivity (NPP), vegetation phenology, climate and permafrost phenology, and analytical methods including partial correlation, multiple linear regression, and path analysis, we explored the response of vegetation growth and phenology to soil freeze-thaw changes and climate change under different degrees of permafrost degradation. Overall, the start date of the growing season (SOS) was very sensitive to the start date of soil thaw (SOT) changes, and multiple regression analyses showed that SOT was the main factor influencing SOS in 41.8% of the NEC region. Climatic factors remain the main factors affecting vegetation NPP in NEC, and the results of partial correlation analysis showed that only 9.7% of the regional duration of soil thaw (DOT) had a strong correlation with vegetation NPP. Therefore, we determined the mechanism responsible for the soil freeze-thaw changes and vegetation growth relationship using the path analysis. The results indicated that there is a potential inhibitory effect of persistent permafrost degradation on vegetation growth. Our findings would contribute to the improvement of process-based models of forest dynamics in the boreal region, which would help to plan sustainable development and conservation strategies in permafrost areas.

Inferring the tree regeneration niche from inventory data using a dynamic forest model

Abstract. The regeneration niche of trees is governed by many processes and factors that are challenging to determine. Besides a species's geographic distribution, which determines if seeds are available, a myriad of local processes in forest ecosystems (e.g., competition and pathogens) exert influences on tree regeneration. Consequently, the representation of tree regeneration in dynamic forest models is a notoriously complicated process which often involves many subprocesses that are often data deficient. The ForClim forest gap model solved this problem by linking species traits to regeneration properties. However, this regeneration module was never validated with large-scale data. Here, we compare this trait-based approach with an inverse calibration approach where we estimate regeneration parameters directly from a large dataset of unmanaged European forests. The inverse calibration was done using Bayesian inference, estimating shade and drought tolerance as well as the temperature requirements for 11 common tree species along with the intensity of regeneration (i.e., the maximum regeneration rate). We find that the parameters determining the species' light niche (i.e., light requirements) are similar for the trait-based and calibrated values for both model variants, but only a more complex model variant that included competition between recruits leads to plausible estimates of the drought niche. The trait-derived temperature niche did not match to the estimates from either model variant using inverse calibration. The parameter estimates differed between the complex and the simple model, with the estimates for the complex model being closer to the trait-based parameters. In both model variants, the calibration strongly changed the parameters that determine regeneration intensity compared to the default. We conclude that the regeneration niche of trees can be recovered from a large forestry dataset in terms of the stand-level parameters light availability and regeneration intensity, while abiotic drivers (temperature and drought) are more elusive. The higher performance (better fit to hold out) of the inversely calibrated models underpins the importance of informing dynamic models by real-world observations. Future research should focus on even greater environmental coverage of observations of demographic processes in unmanaged forests to verify our findings at species range limits under extreme climatic conditions.

Representation of regeneration dynamics in growth and yield models: a review

Models of forest regeneration dynamics have been less widely applied in forest management than those representing growth and mortality in later stages of stand development, in spite of the critical role of regeneration in maintaining forest ecosystems. This omission is demonstrated by a review of pertinent literature and examined in the context of reforestation in the province of Alberta. Regeneration assessments in Alberta are undertaken before the regeneration phase of stand development is complete. As a result, existing growth and yield models, used to predict whether regeneration performance will meet management objectives, do not adequately represent juvenile mortality, ingress of natural regeneration, stand density, and the responses of ingress and mortality to reforestation treatments. A long-term experiment monitoring regeneration of lodgepole pine stands following harvest has over the last 20 years attempted to address some of the resulting challenges. Opportunities and needs for regeneration modelling include extension to other boreal species and ecotypes, incorporation of climatic variables, and innovations in data collection and analytical techniques. Steps are recommended for expediting the required research. 

Forest Carbon Modeling in Poplar and Black Locust Short Rotation Coppice Plantation in Hungary

Forest carbon dynamic modeling for estimating the carbon stock in short rotation coppice bioenergy plantation in Hungary will be vital for better comprehending the role of black locust (Robinia pseudoacacia) and poplar (Populus sp.) in carbon dioxide sequestration from the atmosphere. The research aims were to estimate the potential carbon stock and describe the carbon distribution of the short rotation coppice bioenergy plantation above and below ground. Various sources were used to acquire parameterization data for developing forest carbon dynamic models. CO2FIX modeling V.3.2 was utilized in the data analysis to estimate the total carbon stock in biomass, soil, harvested wood products, and bioenergy compartments. Modeling has been around for 45 years. In this research, the total carbon stock of black locust and poplar at the end of the simulation period was 64.13 and 131.08 MgC.ha-1, respectively. The average carbon allocation above and below ground for black locust and poplar was 0.76, 19.76, 1.80, and 21.67 MgC.ha-1, respectively. In conclusion, poplar outperformed black locust regarding carbon storage in the short rotation coppice bioenergy plantation. Below ground carbon allocation was much higher than above ground. Therefore, more attention should be paid on below ground allocation through environmentally friendly soil management. Keywords: bioenergy plantation, carbon dynamics, climate change mitigation, CO2FIX model, fast growing species

Tree regeneration in models of forest dynamics: A key priority for further research

AbstractTree regeneration is a key process in forest dynamics, particularly in the context of forest resilience and climate change. Models are pivotal for assessing long‐term forest dynamics, and they have been in use for more than 50 years. However, there is a need to evaluate their capacity to accurately represent tree regeneration. We assess how well current models capture the overall abundance, species composition, and mortality of tree regeneration. Using 15 models built to capture long‐term forest dynamics at the stand, landscape, and global levels, we simulate tree regeneration at 200 sites representing large environmental gradients across Central Europe. The results are evaluated against extensive data from unmanaged forests. Most of the models overestimate recruitment levels, which is compensated only in some models by high simulated mortality rates in the early stages of individual‐tree dynamics. Simulated species diversity of recruitment generally matches observed ranges. Models simulating higher stand‐level species diversity do not feature higher species diversity in the recruitment layer. The effect of light availability on recruitment levels is captured better than the effects of temperature and soil moisture, but patterns are not consistent across models. Increasing complexity in the tree regeneration modules is not related to higher accuracy of simulated tree recruitment. Furthermore, individual model design is more important than scale (stand, landscape, and global) and approach (empirical and process‐based) for accurately capturing tree regeneration. Despite the mismatches between simulation results and data, it is remarkable that most models capture the essential features of the highly complex process of tree regeneration, while not having been parameterized with such data. We conclude that much can be gained by evaluating and refining the modeling of tree regeneration processes. This has the potential to render long‐term projections of forest dynamics under changing environmental conditions much more robust.

PyMANGA: A modular, open and extendable software platform for modeling of forest and vegetation dynamics

Agent-based vegetation models are a widely used tool in ecology, for example, to understand and predict the response of vegetation to environmental change. Models are based on well-established descriptions of processes such as vegetation establishment, growth and mortality. However, they are often developed from scratch, which can be inefficient. Here we present pyMANGA, a free and open-source platform for plant growth modelers. pyMANGA's modular design allows for the combination of different concepts and theories of how plants establish, grow or compete in response to above- and below-ground resource availability. New or alternative modules describing, e.g., competition or facilitation, can be easily added. The interchangeability of modules supports the systematic testing of different hypotheses, e.g., on dominant processes in soil-plant feedback loops. Here we further present the thorough benchmarking strategy to maintain the platform and how pyMANGA can be used to compare models with different levels of abstraction and complexity.

Net carbon sequestration implications of intensified timber harvest in Northeastern U.S. forests

AbstractU.S. forests, particularly in the eastern states, provide an important offset to greenhouse gas (GHG) emissions. Some have proposed that forest‐based natural climate solutions can be strengthened via a number of strategies, including increases in the production of forest biomass energy. We used output from a forest dynamics model (SORTIE‐ND) in combination with a GHG accounting tool (ForGATE) to estimate the carbon consequences of current and intensified timber harvest regimes in the Northeastern United States. We considered a range of carbon pools including forest ecosystem pools, forest product pools, and waste pools, along with different scenarios of feedstock production for biomass energy. The business‐as‐usual (BAU) scenario, which represents current harvest practices derived from the analysis of U.S. Forest Service Forest Inventory and Analysis data, sequestered more net CO2 equivalents than any of the intensified harvest and feedstock utilization scenarios over the next decade, the most important time period for combatting climate change. Increasing the intensity of timber harvest increased total emissions and reduced landscape average forest carbon stocks, resulting in reduced net carbon sequestration relative to current harvest regimes. Net carbon sequestration “parity points,” where the regional cumulative net carbon sequestration from alternate intensified harvest scenarios converge with and then exceed the BAU baseline, ranged from 12 to 40 years. A “no harvest” scenario provides an estimate of an upper bound on forest carbon sequestration in the region given the expected successional dynamics of the region's forests but ignores leakage. Regional net carbon sequestration is primarily influenced by (1) the harvest regime and amount of forest biomass removal, (2) the degree to which bioenergy displaces fossil fuel use, and (3) the proportion of biomass diverted to energy feedstocks versus wood products.

Scaling up tree growth to assess forest resilience under increasing aridity: the case of Iberian dry-edge pine forests

ContextMediterranean managed dry-edge pine forests maintain biodiversity and supply key ecosystem services but are threatened by climate change and are highly vulnerable to desertification. Forest management through its effect on stand structure can play a key role on forest stability in response to increasing aridity, but the role of forest structure on drought resilience remains little explored.ObjectivesTo investigate the role of tree growth and forest structure on forest resilience under increasing aridity and two contrasting policy-management regimes. We compared three management scenarios; (i) “business as usual”-based on the current harvesting regime and increasing aridity—and two scenarios that differ in the target forest function; (ii) a “conservation scenario”, oriented to preserve forest stock under increasing aridity; and (iii), a “productivity scenario” oriented to maintain forest yield under increasingly arid conditions.MethodsThe study site is part of a large-homogeneous pine-covered landscape covering sandy flatlands in Central Spain. The site is a dry-edge forest characterized by a lower productivity and tree density relative to most Iberian Pinus pinaster forests. We parameterized and tested an analytical size-structured forest dynamics model with last century tree growth and forest structure historical management records.ResultsUnder current management (Scenario-i), increasing aridity resulted in a reduction of stock, productivity, and maximum mean tree size. Resilience boundaries differed among Scenario-ii and -Scenario-iii, revealing a strong control of the management regime on resilience via forest structure. We identified a trade-off between tree harvest size and harvesting rate, along which there were various possible resilient forest structures and management regimes. Resilience boundaries for a yield-oriented management (Scenario-iii) were much more restrictive than for a stock-oriented management (Scenario-ii), requiring a drastic decrease in both tree harvest size and thinning rates. In contrast, stock preservation was feasible under moderate thinning rates and a moderate reduction in tree harvest size.ConclusionsForest structure is a key component of forest resilience to drought. Adequate forest management can play a key role in reducing forest vulnerability while ensuring a long-term sustainable resource supply. Analytical tractable models of forest dynamics can help to identify key mechanisms underlying drought resilience and to design management options that preclude these social-ecological systems from crossing a tipping point over a degraded alternate state.

Climate-sensitive forecasts of marked short-term and long-term changes in the distributions or abundances of Northwestern boreal landbirds

Climate change presents a major threat to biodiversity globally. Northern ecosystems, such as Canada's boreal forest, are predicted to experience particularly severe climate-induced changes. These changes may reduce the carrying capacity and habitat suitability of the boreal forest for many wildlife species. Boreal birds are susceptible to both direct and indirect effects of climate change, and several studies have predicted northward shifts in species distributions as temperatures become warmer. We forecasted spatially-explicit changes in the densities of 72 boreal landbird species using integrated climate change projections and a forest dynamics model in the Taiga Plains ecozone of the Northwest Territories (NT), Canada, over the 2011–2091 horizon. We 1) identified "winner," "loser," and "bellringer" species over short (2031) and long-term (2091) forecasts, 2) mapped landbird range and density changes under three contrasting Global Circulation Models (GCMs), and 3) quantify differences in landbird density predictions across a latitudinal gradient. Species that showed a moderate increase or decrease in their predicted abundance were considered "winners" and "losers," respectively. Species that showed a marked increase or decrease – a doubling or halving – of their predicted abundance in all three GCMs, were termed "bellringers". From 2011–2031, only 2/72 (2.8%) were considered winners, and 3/72 (4.2%) were losers. From 2011–2091, the abundance of more species was predicted to change: 26/72 (36.1%) were winners, and 10/72 species (13.9%) were losers. Four species were considered bellringers: Gray-cheeked Thrush, White-crowned Sparrow, Fox Sparrow, and American Tree Sparrow. Overall, projected range shifts were strongly oriented along a southeast-to-northwest axis. Shifts to the north and south were evenly distributed among all three GCMs. Our results suggest that future climate-mitigated distribution shifts and population declines of boreal landbirds will require targeted conservation actions. They also highlight the importance of the NT as a potential refugium for many boreal-breeding landbird species in Canada.

Modified SEIB-DGVM enables simulation of masting in a temperate forest

Masting, the periodical mass production of seeds, affects forest ecosystems and food web dynamics. However, masting is not included in process-based models of forest dynamics which makes it difficult to predict changes in masting characteristics owing to climate change. We developed a masting model using the Spatially Explicit Individual Based-Dynamic Global Vegetation Model and validated it in a temperate forest containing Quercus crispula. Four models were devised using combinations of three major masting theories: resource budget, pollen limitation, and weather pattern hypotheses. The models simulated masting by modifying the reproduction of a novel plant functional type by parameterising the resource allocation to masting, the threshold limit for flowering, and the cost of acorn production. The results indicate that masting affected tree mortality; when trees allocated too many resources to masting and the threshold limit for flowering was low, tree mortality of the masting group increased. The model based on the resource budget and pollen limitation hypotheses corresponded most closely to observations, particularly under parameter combinations involving low resource allocation to masting and cost of acorn production. The models can be adapted to other species and for global applications, such as simulating the effects of climate change on masting and mitigating negative impacts on forest ecosystems and human–wildlife interactions.

Modeling Management-Relevant Urban Forest Stand Characteristics to Optimize Carbon Storage and Sequestration

Urban forests are an essential part of adaptation and mitigation solutions for climate change. To understand the relationship between carbon storage, sequestration, and stand density in the most heavily-managed aspect of the urban forest—street trees—we modified the parameters and algorithms of a rural forest dynamics model, the perfect plasticity approximation (PPA), to reflect urban street tree conditions. The main changes in the new street tree PPA are the maintenance of a prescribed stand density via management of recruitment, the possibility of crown-roof overlap, and increased mortality rates. Using the street tree PPA, we explored overall productivity, crown allometry relative to stem diameter, and mortality rate to test each mechanism’s impact on urban street tree carbon storage and sequestration across a gradient of prescribed stand density, with the goal of finding conditions in which street tree carbon storage and sequestration are optimized. We compared the qualitative trends in storage from the street tree PPA to those found in the U.S. Forest Service’s Urban Forest Inventory Analysis data. We found that carbon storage and sequestration increase with prescribed density up to a point where carbon storage and sequestration saturate. Optimized carbon storage and sequestration result from a stand with high productivity, maximized crown allometry relative to stem diameter, and a low mortality rate. These insights can be used to inform urban street tree maintenance strategies that effectively increase carbon storage and sequestration within a given city, such as focusing afforestation campaigns on adequate areas with the lowest street tree densities.

LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data

Even where Reduced-Impact Logging (RIL) practices are applied, selective logging causes substantial damage to tropical forests. To further reduce selective logging damage, the practices that cause the most damage need to be identified and alternatives tested. To this end, we developed the R package LoggingLab, a spatially-explicit and individual tree-based selective logging simulator and demonstrated its functions using data from French Guiana. LoggingLab explicitly simulates damage during each stage of the selective logging process taking into account topography and hydrography, which are main constraints on logging. Most LoggingLab parameters can be easily adjusted to a wide range of local contexts. LoggingLab can also be coupled with forest dynamics models to simulate the long- term effects of different selective logging scenarios.

The umbrella value of caribou management strategies for biodiversity conservation in boreal forests under global change

Single-species conservation management is often proposed to preserve biodiversity in human-disturbed landscapes. How global change will impact the umbrella value of single-species management strategies remains an open question of critical conservation importance. We assessed the effectiveness of threatened boreal caribou as an umbrella for bird and beetle conservation under global change. We combined mechanistic, spatially explicit models of forest dynamics and predator-prey interactions to forecast the impact of management strategies on the survival of boreal caribou in boreal forest. We then used predictive models of species occupancy to characterize concurrent impacts on bird and beetle diversity. Landscapes were simulated based on three scenarios of climate change and four of forest management. We found that strategies that best mitigate human impact on boreal caribou were an effective umbrella for maintaining bird and beetle assemblages. While we detected a stronger effect of land-use change compared to climate change, the umbrella value of management strategies for caribou habitat conservation were still impacted by the severity of climate change. Our results showed an interplay among changes in forest attributes, boreal caribou mortality, as well as bird and beetle species assemblages. The conservation status of some species mandates the development of recovery strategies, highlighting the importance of our study which shows that single-species conservation can have important umbrella benefits despite global change.

Recruitment models after reduced impact logging in the Amazon rainforest

Recruitment models in rainforests are important for studies on forest management sustainability and can guide the assessment of remaining stocks and timber harvesting. Therefore, the objective of this study was to estimate recruitment after Reduced Impact Logging (RIL) by using two categories of models: a distance-independent individual-tree model (ITM) and a matrix model of forest dynamics or transition matrix (TMM). Data from continuous inventories conducted in a Dense Ombrophilous Forest in Paragominas, Pará state, Brazil, were utilized. Two transects of 9 ha each were installed at an area with Reduced Impact Logging, the data was collected in 2005 (nine months after logging), then in 2006, 2008, 2010 and 2012. Considering a period of 8 years after RIL, the individual tree-based model and transition matrix give consistent predictions of the recruitment in the study area. However, we observed slight superiority by the ITM, which presented values closer to the real ones for the total number of trees per hectare.

Rcontroll: An R interface for the individual‐based forest dynamics simulator TROLL

Abstract A central challenge in ecology is understanding the emergence of patterns as the result of interactions among individuals. Dynamic forest models can provide a fine‐scale description of the ecological, physiological and environmental processes that explain the demography of coexisting tree species. This in turn helps predict changes under future scenarios. However, model accessibility is a major obstacle to a wide use and communication across scientific disciplines and for educational purposes. Here, we present the R package rcontroll, which provides access to the TROLL forest simulator in the R environment. TROLL is individual‐based and spatially explicit and leverages knowledge of ecology, biogeochemistry and tree ecophysiology through a trait‐based parameterisation. TROLL has been used to simulate carbon fluxes and tree diversity in tropical and subtropical forests and to explore forest resilience to disturbance and environmental changes more generally. rcontroll provides a user‐friendly environment to set up and analyse TROLL simulations with varying community compositions, ecological parameters and climate conditions. We show how to test parameter sensitivity in TROLL using the rcontroll R package. We also demonstrate the flexibility and ease of use of rcontroll by replicating a previously published study based on the TROLL simulator. Both examples are included with reproducible code documents. Complex forest simulators are important scientific tools for science and education, and wide access to these tools is an important condition for their adoption. TROLL is designed to address a wide range of ecological and environmental questions, and the new R package rcontroll is designed to be an entry point for TROLL model users.

Contrasting impacts of climate change on protection forests of the Italian Alps

Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.