Model Of Forest Dynamics Research Articles

Effect of different tree mortality patterns on stand development in the forest model SIBYLA

Abstract Forest mortality critically affects stand structure and the quality of ecosystem services provided by forests. Spruce bark beetle (Ips typographus) generates rather complex infestation and mortality patterns, and implementation of such patterns in forest models is challenging. We present here the procedure, which allows to simulate the bark beetle-related tree mortality in the forest dynamics model Sibyla. We explored how sensitive various production and stand structure indicators are to tree mortality patterns, which can be generated by bark beetles. We compared the simulation outputs for three unmanaged forest stands with 40, 70 and 100% proportion of spruce as affected by the disturbance-related mortality that occurred in a random pattern and in a patchy pattern. The used tree species and age class-specific mortality rates were derived from the disturbance-related mortality records from Slovakia. The proposed algorithm was developed in the SQLite using the Python language, and the algorithm allowed us to define the degree of spatial clustering of dead trees ranging from a random distribution to a completely clustered distribution; a number of trees that died in either mode is set to remain equal. We found significant differences between the long-term developments of the three investigated forest stands, but we found very little effect of the tested mortality modes on stand increment, tree species composition and diversity, and tree size diversity. Hence, our hypothesis that the different pattern of dead trees emergence should affect the competitive interactions between trees and regeneration, and thus affect selected productivity and stand structure indicators was not confirmed.

Conceptual models of forest dynamics in environmental education and management: keep it as simple as possible, but no simpler

Conceptual models of forest dynamics are powerful cognitive tools, which are indispensable for communicating ecological ideas and knowledge, and in developing strategic approaches and setting targets for forest conservation, restoration and sustainable management. Forest development through time is conventionally described as a directional, or “linear”, and predictable sequence of stages from “bare ground” to old forest representing the “climax-state”. However, this simple view is incompatible with the current knowledge and understanding of intrinsic variability of forest dynamics. Overly simple conceptual models of forest dynamics easily become transformed into biased mental models of how forests naturally develop and what kind of structures they display. To be able to communicate the essential features and diversity of forest dynamics, comprehensive conceptual models are needed. For this end, Kuuluvainen (2009) suggested a relatively simple conceptual model of forest dynamics, which separates three major modes of forest dynamics, and incorporates state changes and transitions between the forest dynamics modes depending on changes in disturbance regime. Conceptual models of forest dynamics should be comprehensive enough to incorporate both long-term directional change and short-term cyclic forest dynamics, as well as transitions from one dynamics mode to another depending on changes in the driving disturbance regime type. Models that capture such essential features of forest dynamics are indispensable for educational purposes, in setting reference conditions and in developing methods in forest conservation, restoration and ecosystem management.

From monocultures to mixed-species forests: is tree diversity key for providing ecosystem services at the landscape scale?

Converting monocultures to mixed-species stands is thought to be a promising approach to increase forest productivity and resilience, while additionally providing other ecosystem goods and services (EGS). However, the importance of tree species composition and structure remains unclear, particularly beyond the stand scale due to the difficulty of conducting comprehensive, long-term experiments. To compare the ability of different tree species mixtures to provide various EGS at the landscape scale. We used a dynamic forest landscape model to simulate all possible combinations of dominant tree species for two landscapes; a high-elevation alpine region (Dischma valley, Switzerland) and a lowland valley (Mt. Feldberg, Germany). We evaluated multiple EGS, including protection from gravitational hazards, aboveground biomass, and habitat quality, and examined trade-offs and synergies between them. Mixed-species forests were usually better in providing multiple EGS, although monocultures were often best for single EGS. The simulation results also demonstrated how changing environmental conditions along an elevational gradient had a strong impact on the structure of different species combinations and therefore on the provisioning of EGS. Tree species diversity alone is not a good predictor of multifunctionality. Mixtures should be selected based on local environmental conditions, complementary functional traits, and the ability to provide the EGS of interest. Although our work focused on current climatic conditions, we discuss how the modelling framework could be employed to consider the impacts of climate change and disturbances to improve our understanding of how mixed-species stands could be used to cope with these challenges.

Climate change amplifies the interactions between wind and bark beetle disturbances in forest landscapes

ContextGrowing evidence suggests that climate change could substantially alter forest disturbances. Interactions between individual disturbance agents are a major component of disturbance regimes, yet how interactions contribute to their climate sensitivity remains largely unknown.ObjectivesHere, our aim was to assess the climate sensitivity of disturbance interactions, focusing on wind and bark beetle disturbances.MethodsWe developed a process-based model of bark beetle disturbance, integrated into the dynamic forest landscape model iLand (already including a detailed model of wind disturbance). We evaluated the integrated model against observations from three wind events and a subsequent bark beetle outbreak, affecting 530.2 ha (3.8 %) of a mountain forest landscape in Austria between 2007 and 2014. Subsequently, we conducted a factorial experiment determining the effect of changes in climate variables on the area disturbed by wind and bark beetles separately and in combination.ResultsiLand was well able to reproduce observations with regard to area, temporal sequence, and spatial pattern of disturbance. The observed disturbance dynamics was strongly driven by interactions, with 64.3 % of the area disturbed attributed to interaction effects. A +4 °C warming increased the disturbed area by +264.7 % and the area-weighted mean patch size by +1794.3 %. Interactions were found to have a ten times higher sensitivity to temperature changes than main effects, considerably amplifying the climate sensitivity of the disturbance regime.ConclusionsDisturbance interactions are a key component of the forest disturbance regime. Neglecting interaction effects can lead to a substantial underestimation of the climate change sensitivity of disturbance regimes.

Predicting the responses of forest distribution and aboveground biomass to climate change under RCP scenarios in southern China.

In the past three decades, our global climate has been experiencing unprecedented warming. This warming has and will continue to significantly influence the structure and function of forest ecosystems. While studies have been conducted to explore the possible responses of forest landscapes to future climate change, the representative concentration pathways (RCPs) scenarios under the framework of the Coupled Model Intercomparison Project Phase 5 (CMIP5) have not been widely used in quantitative modeling research of forest landscapes. We used LANDIS-II, a forest dynamic landscape model, coupled with a forest ecosystem process model (PnET-II), to simulate spatial interactions and ecological succession processes under RCP scenarios, RCP2.6, RCP4.5 and RCP8.5, respectively. We also modeled a control scenario of extrapolating current climate conditions to examine changes in distribution and aboveground biomass (AGB) among five different forest types for the period of 2010-2100 in Taihe County in southern China, where subtropical coniferous plantations dominate. The results of the simulation show that climate change will significantly influence forest distribution and AGB. (i) Evergreen broad-leaved forests will expand into Chinese fir and Chinese weeping cypress forests. The area percentages of evergreen broad-leaved forests under RCP2.6, RCP4.5, RCP8.5 and the control scenarios account for 18.25%, 18.71%, 18.85% and 17.46% of total forest area, respectively. (ii) The total AGB under RCP4.5 will reach its highest level by the year 2100. Compared with the control scenarios, the total AGB under RCP2.6, RCP4.5 and RCP8.5 increases by 24.1%, 64.2% and 29.8%, respectively. (iii) The forest total AGB increases rapidly at first and then decreases slowly on the temporal dimension. (iv) Even though the fluctuation patterns of total AGB will remain consistent under various future climatic scenarios, there will be certain responsive differences among various forest types.

Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification

The advent of mobile laser scanning has enabled time efficient and cost effective collection of forest structure information. To make use of this technology in calibrating or evaluating models of forest and landscape dynamics, there is a need to systematically and reproducibly automate the processing of LiDAR point clouds into quantities of forest structural components. Here we propose a method to classify vegetation structural components of an open-understorey eucalyptus forest, scanned with a ‘Zebedee’ mobile laser scanner. It detected 98% of the tree stems (N = 50) and 80% of the elevated understorey components (N = 15). Automatically derived DBH values agreed with manual field measurements with r2 = 0.72, RMSE = 3.8 cm, (N = 27), and total basal area agreed within 1.5%. Though this methodological study was restricted to one ecosystem, the results are promising for use in applications such as fuel load, habitat structure, and biomass estimations.

Tree Size Inequality Reduces Forest Productivity: An Analysis Combining Inventory Data for Ten European Species and a Light Competition Model.

Plant structural diversity is usually considered as beneficial for ecosystem functioning. For instance, numerous studies have reported positive species diversity-productivity relationships in plant communities. However, other aspects of structural diversity such as individual size inequality have been far less investigated. In forests, tree size inequality impacts directly tree growth and asymmetric competition, but consequences on forest productivity are still indeterminate. In addition, the effect of tree size inequality on productivity is likely to vary with species shade-tolerance, a key ecological characteristic controlling asymmetric competition and light resource acquisition. Using plot data from the French National Geographic Agency, we studied the response of stand productivity to size inequality for ten forest species differing in shade tolerance. We fitted a basal area stand production model that included abiotic factors, stand density, stand development stage and a tree size inequality index. Then, using a forest dynamics model we explored whether mechanisms of light interception and light use efficiency could explain the tree size inequality effect observed for three of the ten species studied. Size inequality negatively affected basal area increment for seven out of the ten species investigated. However, this effect was not related to the shade tolerance of these species. According to the model simulations, the negative tree size inequality effect could result both from reduced total stand light interception and reduced light use efficiency. Our results demonstrate that negative relationships between size inequality and productivity may be the rule in tree populations. The lack of effect of shade tolerance indicates compensatory mechanisms between effect on light availability and response to light availability. Such a pattern deserves further investigations for mixed forests where complementarity effects between species are involved. When studying the effect of structural diversity on ecosystem productivity, tree size inequality is a major facet that should be taken into account.

The potential to restore native woody plant richness and composition in a reforesting landscape: a modeling approach in the Ecuadorian Andes

Natural regenerating forests are rapidly expanding in the tropics. Forest transitions have the potential to restore biodiversity. Spatial targeting of land use policies could improve the biodiversity benefits of reforesting landscapes. We explored the relative importance of landscape attributes in influencing the potential of tree cover increase to restore native woody plant biodiversity at the landscape scale. We developed land use scenarios that differed in spatial patterns of reforestation, using the Pangor watershed in the Ecuadorian Andes as a case study. We distinguished between reforestation through natural regeneration of woody vegetation in abandoned fallows and planted forests through managed plantations of exotic species on previously cultivated land. We simulated the restoration of woody plant biodiversity for each scenario using LANDIS-II, a process-based model of forest dynamics. A pair-case comparison of simulated woody plant biodiversity for each scenario was conducted against a random scenario. Species richness in natural regenerating fallows was considerably higher when occurring in: (i) close proximity to remnant forests; (ii) areas with a high percentage of surrounding forest cover; and (iii) compositional heterogeneous landscapes. Reforestation at intermediate altitudes also positively affected restoration of woody plant species. Planted exotic pine forests negatively affected species restoration. Our research contributes to a better understanding of the recolonization processes of regenerating forests. We provide guidelines for reforestation policies that aim to conserve and restore woody plant biodiversity by accounting for landscape attributes.

Using a dynamic forest model to predict tree species distributions

AbstractAimIt has been suggested that predicting species distributions requires a process‐based and preferably dynamic approach. If dynamic models are to contribute towards understanding species distributions, uncertainties related to their spatial extrapolation and bioclimatic parameters need to be addressed. Here, we analyse the potential of a forest gap model for predicting species distributions.LocationPacific Northwest of North America (PNW).MethodsWe used the dynamic forest gap modelForClim, which includes climate, competition and demographic processes, to simulate the distribution of 18 tree species outside the domain of the data used for fitting. We explored model accuracy for species distributions at the regional scale by: (1) estimating species climatic tolerances so as to maximize agreement with regional distribution maps versus (2) employing a bioclimatic parameter set that produces high accuracy at the local scale. We then performed the opposite tests and simulated local forest composition in a small area in thePNW, using (3) the local bioclimatic parameters and (4) the bioclimatic parameters that produced the highest accuracy at the regional scale. We also compared theForClimresults with predictions from a standard correlative species distribution model (SDM).ResultsForClimproduced regional species distributions with fair to very good agreement for 12 tree species. The optimized bioclimatic parameters consistently improved the accuracy of regional predictions compared with simulations run with the local parameters, and were consistent withSDMresults. At the local scale, predictions using the local parameters conformed to descriptions of forest composition, but accuracy decreased strongly when using the regionally calibrated parameters.Main conclusionsForest gap models can predict regional species distributions, but at the cost of reduced accuracy at the local scale. Future applications of gap models to understand regional species distributions should include robust parameterization schemes and additional ecological processes that are important at large spatial scales (e.g. dispersal, disturbances).

Interactions among spruce beetle disturbance, climate change and forest dynamics captured by a forest landscape model

The risk of bark beetle outbreaks is widely predicted to increase because of a warming climate that accelerates temperature‐driven beetle population growth and drought stress that impairs host tree defenses. However, few if any studies have explicitly evaluated climatically enhanced beetle population dynamics in relation to climate‐driven changes in forest composition and structure that may alter forest suitability for beetle infestation. We synthesized current understanding of the interactions among climate, spruce beetles (Dendroctonus rufipennis) and forest dynamics to parameterize and further advance the bark beetle module of a dynamic forest landscape model (LandClim) that also integrates fire and wind disturbance and climate‐driven forest succession. We applied the model to a subalpine watershed in northwestern Colorado to examine the mechanisms and feedbacks that may lead to shifts in forest composition and spruce beetle disturbance under three climate change scenarios. Simulation results suggest increased drought‐ and beetle‐induced reduction of large Engelmann spruce (Picea engelmannii) trees while Douglas‐fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa) increased in dominance throughout the study area under all climate change scenarios. This shift in forest composition and structure counterbalances the enhancing effects of accelerated beetle population development and increased drought‐induced susceptibility of spruce to beetles. As a result, we projected a long‐term decrease in beetle‐induced spruce mortality to below historical values under all climate scenarios at low elevations (<2800 m asl). Beetle‐induced spruce mortality above 2800 m asl and under moderate climate change was slightly higher and more variable than under historical conditions but decreased to 36% and 6% of historical values under intermediate and extreme climate change, respectively. Because mechanisms driving beetle disturbance dynamics are similar across different bark beetle species, we argue that the depletion of host trees due to drought and beetle disturbance may also be important in other climate‐sensitive beetle‐host systems. We advocate for the consideration of climate‐driven shifts in forest and disturbance dynamics in devising adaptive management strategies.

Long-term effects of traditional and conservation-oriented forest management on the distribution of vertebrates in Mediterranean forests: a hierarchical hybrid modelling approach

Aim Recently, increasing attention has been devoted to the development of sustainable forestry practices aimed at finding a balance between the maintenance and enhancement of different forest resources. However, the long-term, large-scale effects of conservation-oriented forest management on vertebrates have been poorly studied. We tested the hypothesis that conservation-oriented forest management, being conceived to mimic the dynamics of a natural forest succession more closely than does traditional forestry, causes a less severe long-term impact on the distribution of forest vertebrates. Location Molise region, Central Italy. Methods We proposed a hybrid modelling framework based on the integration of a forest dynamic model (‘LANDIS-II’) with species distribution models (SDMs). The framework was applied on four forest specialist vertebrates (tiny salamander, slow worm, Eurasian nuthatch and Leisler's bat) and included three components: forest dynamic model (FDM), SDMs and spatial pattern analysis (SPA). FDM was used to simulate spatially explicit patterns of forest succession for the current time and for 2050, imposing three alternative forestry scenarios. The simulated forest succession patterns were analysed through SPA to calculate spatialized landscape metrics that were adopted as environmental predictors for SDMs. Landscape trajectories were calculated on current and future species distributions predicted with SDMs to evaluate the effect of alternative forestry practices on their extent and fragmentation. Results Forest management mainly affected the spatial configuration rather than the extent of the species potential distributions. Conservation-oriented forest management was more favourable than traditional forestry in increasing the extent and reducing the fragmentation of the studied species’ distributions. Main conclusions Conservation-oriented forest management, by mimicking the dynamics of a natural forest succession more closely than did traditional practices, favoured elements, such as forest unevenness, species richness, aggregation of patches and variability in their distances, which emerged as fundamental characteristics for preserving the long-term persistence of forest vertebrates.

Applying ecological model evaludation: Lessons learned with the forest dynamics model Samsara2

Ecological models are increasingly used as decision-making tools and their reliability is becoming a key issue. At the same time, the sophistication of techniques for model development and analysis has given rise to a relative compartmentalization of model building and evaluation tasks. Several guidelines invite ecological modelers to follow an organized sequence of development and analysis steps and have coined the term “evaludation” for this process. The objective of this paper is to assess the feasibility and the value of a structured evaludation process, based on the working example of the Samsara2 model, a spatially explicit individual-based forest dynamics model. We implemented the six steps of model design, process level calibration, qualitative evaluation, quantitative evaluation, global sensitivity analysis, and partial recalibration using approximate Bayesian computing. We then evaluated how the evaludation process revealed model strengths and weaknesses, specified the model's conditions of use, clarified how the model works, and provided insights into forest ecosystem functioning. Finally, the efficiency/cost ratio of the process and future improvements are discussed.

Species richness promotes canopy packing: a promising step towards a better understanding of the mechanisms driving the diversity effects on forest functioning

Species richness promotes canopy packing: a promising step towards a better understanding of the mechanisms driving the diversity effects on forest functioning

Temporal trends and sources of variation in carbon flux from coarse woody debris in experimental forest canopy openings.

Pulses of respiration from coarse woody debris (CWD) have been observed immediately following canopy disturbances, but it is unclear how long these pulses are sustained. Several factors are known to influence carbon flux rates from CWD, but few studies have evaluated more than temperature and moisture. We experimentally manipulated forest structure in a second-growth northern hardwood forest and measured CO2 flux periodically for seven growing seasons following gap creation. We present an analysis of which factors, including the composition of the wood-decay fungal community influence CO2 flux. CO2 flux from CWD was strongly and positively related to wood temperature and varied significantly between substrate types (logs vs. stumps). For five growing seasons after treatment, the CO2 flux of stumps reached rates up to seven times higher than that of logs. CO2 flux of logs did not differ significantly between canopy-gap and closed-canopy conditions in the fourth through seventh post-treatment growing seasons. By the seventh season, the seasonal carbon flux of both logs and stumps had decreased significantly from prior years. Linear mixed models indicated the variation in the wood inhabiting fungal community composition explained a significant portion of variability in the CO2 flux along with measures of substrate conditions. CO2 flux rates were inversely related to fungal diversity, with logs hosting more species but emitting less CO2 than stumps. Overall, our results suggest that the current treatment of CWD in dynamic forest carbon models may be oversimplified, thereby hampering our ability to predict realistic carbon fluxes associated with wood decomposition.

Modelling and predicting the spatial distribution of tree root density in heterogeneous forest ecosystems.

In mountain ecosystems, predicting root density in three dimensions (3-D) is highly challenging due to the spatial heterogeneity of forest communities. This study presents a simple and semi-mechanistic model, named ChaMRoots, that predicts root interception density (RID, number of roots m(-2)). ChaMRoots hypothesizes that RID at a given point is affected by the presence of roots from surrounding trees forming a polygon shape. The model comprises three sub-models for predicting: (1) the spatial heterogeneity - RID of the finest roots in the top soil layer as a function of tree basal area at breast height, and the distance between the tree and a given point; (2) the diameter spectrum - the distribution of RID as a function of root diameter up to 50 mm thick; and (3) the vertical profile - the distribution of RID as a function of soil depth. The RID data used for fitting in the model were measured in two uneven-aged mountain forest ecosystems in the French Alps. These sites differ in tree density and species composition. In general, the validation of each sub-model indicated that all sub-models of ChaMRoots had good fits. The model achieved a highly satisfactory compromise between the number of aerial input parameters and the fit to the observed data. The semi-mechanistic ChaMRoots model focuses on the spatial distribution of root density at the tree cluster scale, in contrast to the majority of published root models, which function at the level of the individual. Based on easy-to-measure characteristics, simple forest inventory protocols and three sub-models, it achieves a good compromise between the complexity of the case study area and that of the global model structure. ChaMRoots can be easily coupled with spatially explicit individual-based forest dynamics models and thus provides a highly transferable approach for modelling 3-D root spatial distribution in complex forest ecosystems.

Modelling the effect of climate-induced changes in recruitment and juvenile growth on mixed-forest dynamics: The case of montane–subalpine Pyrenean ecotones

Most predictive models forecast significant upward displacement of forest species due to increases in temperatures, but not all the species respond in the same way to changes in climate. In temperate or mountain systems, biotic competitive interactions drive species distributions, and responses to climate change will ultimately depend upon productive and demographic processes such as growth, recruitment and mortality. We parameterized and used an individual-based, spatially explicit model of forest dynamics (SORTIE-ND) to investigate the role of species-specific differences in juvenile performance induced by climate change (juvenile growth and recruitment ability) in the dynamics of mixed forests located in the montane–subalpine ecotone of the Pyrenees. We assessed this role for two types of forests composed of three species with differing light requirements and sensitivity to climate change: (1) a mixed forest with two shade-intolerant pines (Pinus uncinata and Pinus sylvestris) and (2) a mixed forest composed by a shade-intolerant pine and a shade-tolerant fir (Abies alba). Our results show that for species with similar light requirements (i.e., both pines), small differences in sapling growth response to climate change can lead to significant differences in future species composition (an increase in P. sylvestris growth of 10% leads to an increase in its abundance from 42% to 50.3%). Conversely, in pine-fir forests, shade-tolerance results more decisive than climate-induced changes in growth in driving the future forest composition.

The agony of choice: different empirical mortality models lead to sharply different future forest dynamics.

Dynamic models are pivotal for projecting forest dynamics in a changing climate, from the local to the global scale. They encapsulate the processes of tree population dynamics with varying resolution. Yet, almost invariably, tree mortality is modeled based on simple, theoretical assumptions that lack a physiological and/or empirical basis. Although this has been widely criticized and a growing number of empirically derived alternatives are available, they have not been tested systematically in models of forest dynamics. We implemented an inventory-based and a tree-ring-based mortality routine in the forest gap model ForClim v3.0. We combined these routines with a stochastic and a deterministic approach for the determination of tree status (alive vs. dead). We tested the four new model versions for two Norway spruce forests in the Swiss Alps, one of which was managed (inventory time series spanning 72 years) and the other was unmanaged (41 years). Furthermore, we ran long-term simulations (-400 years) into the future under three climate scenarios to test model behavior under changing environmental conditions. The tests against inventory data showed an excellent match of simulated basal area and stem numbers at the managed site and a fair agreement at the unmanaged site for three of the four empirical mortality models, thus rendering the choice of one particular model difficult. However, long-term simulations under current climate revealed very different behavior of the mortality models in terms of simulated changes of basal area and stem numbers, both in timing and magnitude, thus indicating high sensitivity of simulated forest dynamics to assumptions on tree mortality. Our results underpin the potential of using empirical mortality routines in forest gap models. However, further tests are needed that span other climatic conditions and mixed forests. Short-term simulations to benchmark model behavior against empirical data are insufficient; long-term tests are needed that include both nonequilibrium and equilibrium conditions. Thus, there is the potential to greatly improve the robustness of future projections of forest dynamics via more reliable tree mortality submodels.

A joint individual-based model coupling growth and mortality reveals that tree vigor is a key component of tropical forest dynamics.

Tree vigor is often used as a covariate when tree mortality is predicted from tree growth in tropical forest dynamic models, but it is rarely explicitly accounted for in a coherent modeling framework. We quantify tree vigor at the individual tree level, based on the difference between expected and observed growth. The available methods to join nonlinear tree growth and mortality processes are not commonly used by forest ecologists so that we develop an inference methodology based on an MCMC approach, allowing us to sample the parameters of the growth and mortality model according to their posterior distribution using the joint model likelihood. We apply our framework to a set of data on the 20-year dynamics of a forest in Paracou, French Guiana, taking advantage of functional trait-based growth and mortality models already developed independently. Our results showed that growth and mortality are intimately linked and that the vigor estimator is an essential predictor of mortality, highlighting that trees growing more than expected have a far lower probability of dying. Our joint model methodology is sufficiently generic to be used to join two longitudinal and punctual linked processes and thus may be applied to a wide range of growth and mortality models. In the context of global changes, such joint models are urgently needed in tropical forests to analyze, and then predict, the effects of the ongoing changes on the tree dynamics in hyperdiverse tropical forests.

Predicting dispersal of auto-gyrating fruit in tropical trees: a case study from the Dipterocarpaceae.

Seed dispersal governs the distribution of plant propagules in the landscape and hence forms the template on which density-dependent processes act. Dispersal is therefore a vital component of many species coexistence and forest dynamics models and is of applied value in understanding forest regeneration. Research on the processes that facilitate forest regeneration and restoration is given further weight in the context of widespread loss and degradation of tropical forests, and provides impetus to improve estimates of seed dispersal for tropical forest trees. South-East Asian lowland rainforests, which have been subject to severe degradation, are dominated by trees of the Dipterocarpaceae family which constitute over 40% of forest biomass. Dipterocarp dispersal is generally considered to be poor given their large, gyration-dispersed fruits. However, there is wide variability in fruit size and morphology which we hypothesize mechanistically underpins dispersal potential through the lift provided to seeds mediated by the wings. We explored experimentally how the ratio of fruit wing area to mass (“inverse wing loading,” IWL) explains variation in seed dispersal kernels among 13 dipterocarp species by releasing fruit from a canopy tower. Horizontal seed dispersal distances increased with IWL, especially at high wind speeds. Seed dispersal of all species was predominantly local, with 90% of seed dispersing <10 m, although maximum dispersal distances varied widely among species. We present a generic seed dispersal model for dipterocarps based on attributes of seed morphology and provide modeled seed dispersal kernels for all dipterocarp species with IWLs of 1–50, representing 75% of species in Borneo.

Data-intensive modeling of forest dynamics

Forest dynamics are highly dimensional phenomena that are not fully understood theoretically. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a data-intensive methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.