Complex Forest Research Articles

Fire Occurrence in Hemi-Boreal Forests: Exploring Natural and Cultural Scots Pine Fire Regimes Using Dendrochronology in Lithuania

Fire is an important natural disturbance and a driver of hemi-boreal forest successional trajectories, structural complexity, and biodiversity. Understanding the historic fire regime is an important step towards sustainable forest management. Focusing on Lithuania’s hemi-boreal forests, we first mapped the potential natural fire regimes based on the relationship between site conditions, vegetation, and fire frequency using the ASIO model. The ASIO model revealed that all the fire frequency categories (Absent, Seldom, Intermittent, Often) are found in Lithuania. Scots pine forests dominated the often fire frequency category (92%). Secondly, focusing on a fire-prone forest landscape, Dzūkija, we analyzed the fire occurrence of Scots pine forest types using dendrochronological records. We sampled and cross-dated 132 Scots pine samples with fire scars from four dry forest stands (n = 92) and four peatland forest stands (n = 40), respectively. In total, the fire history analysis revealed 455 fire scars and 213 fire events during the period of 1742–2019. The Weibull median fire intervals were 2.7 years (range 1–34) for the dry forest types and 6.3 years (range 1–27) for the peatland forest types. Analysis pre- and post-1950 showed the Weibull median fire interval increased from 2.2 to 7.2 for the dry forest types but decreased from 6.2 to 5.2. for the peatland forest types. A superposed epoch analysis revealed significant precipitation fluxes prior to the fire events after 1950. Thus, the Dzūkija landscape of Lithuania has been strongly shaped by both human and naturally induced fires. The combination of theory (the ASIO model) with the examination of biological archives can be used to help guide sustainable forest management to emulate forest disturbances related to fire. As traditional forest management focusing on wood production has eliminated fire, and effectively simplified forest ecosystems, we recommend introducing educational programs to communicate the benefits and history of forest fires as well as adaptive management trials that use low-intensity prescribed burning of Scots pine stands.

Light heterogeneity affects understory plant species richness in temperate forests supporting the heterogeneity-diversity hypothesis.

One of the most important drivers for the coexistence of plant species is the resource heterogeneity of a certain environment, and several studies in different ecosystems have supported this resource heterogeneity–diversity hypothesis. However, to date, only a few studies have measured heterogeneity of light and soil resources below forest canopies to investigate their influence on understory plant species richness. Here, we aim to determine (1) the influence of forest stand structural complexity on the heterogeneity of light and soil resources below the forest canopy and (2) whether heterogeneity of resources increases understory plant species richness. Measures of stand structural complexity were obtained through inventories and remote sensing techniques in 135 1‐ha study plots of temperate forests, established along a gradient of forest structural complexity. We measured light intensity and soil chemical properties on six 25 m² subplots on each of these 135 plots and surveyed understory vegetation. We calculated the coefficient of variation of light and soil parameters to obtain measures of resource heterogeneity and determined understory plant species richness at plot level. Spatial heterogeneity of light and of soil pH increased with higher stand structural complexity, although heterogeneity of soil pH did not increase in conditions of generally high levels of light availability. Increasing light heterogeneity was also associated with increasing understory plant species richness. However, light heterogeneity had no such effects in conditions where soil resource heterogeneity (variation in soil C:N ratios) was low. Our results support the resource heterogeneity–diversity hypothesis for temperate forest understory at the stand scale. Our results also highlight the importance of interaction effects between the heterogeneity of both light and soil resources in determining plant species richness.

Quantifying the Impact of Management on the Structural Complexity of Boreal Forests

Quantifying the Impact of Management on the Structural Complexity of Boreal Forests

Anthropogenic pressures decrease structural complexity in Caucasian forests of Iran

ABSTRACT The Arasbaran Protected Area of northern Iran encompasses 160,000 ha of deciduous, broad-leaved forest with 78,560 ha recently designated as a United Nations Educational, Scientific, and Cultural Organization’s (UNESCO) Biosphere Reserve. Our study objectives were to (1) quantify whether anthropogenic pressures measurably impacted the structural complexity of Arasbaran forests and (2) determine which structural complexity indices were most sensitive to anthropogenic pressures in Arasbaran forests. We identified and sampled three study sites with high, moderate, and low levels of anthropogenic pressures and used nine forest structural indices to develop structural complexity indices. High anthropogenic pressure resulted in aggregated tree distribution, uniformity in tree sizes, and less heterogeneous forest structure. The structural indices particularly sensitive to anthropogenic pressure were diameter differentiation, height differentiation, Gini coefficient, and structural diversity. Structural complexity appears to be a useful method for quantifying the impacts of anthropogenic pressure on montane deciduous forests. Regular sampling of the diameter differentiation index, height differentiation index, Gini coefficient and structural diversity index should be integrated into conservation management plans for the Arasbaran Protected Area.

Challenges to aboveground biomass prediction from waveform lidar

Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD—that forest stands with different AGBD may produce highly similar waveforms—and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non-uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7–411 Mg ha−1, average of 167 Mg ha−1). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.

Does anthropogenic fragmentation selectively filter avian phylogenetic diversity in a critically endangered forest system?

SummaryDocumenting phylogenetic diversity for conservation practice allows elucidation of ecosystem functioning and processes by highlighting the commonality and divergence of species’ functional traits within their evolutionary context. Conserving distinct evolutionary histories has intrinsic value, and the conservation of phylogenetically diverse communities is more likely to preserve distinct or relic evolutionary lineages. We explored the potential for anthropogenic forest fragmentation to act as a selective filter of avian phylogenetic diversity within the community of forest-dependent birds of the critically endangered Indian Ocean Coastal Belt Forest (IOCBF), South Africa. We conducted avian point count surveys during the austral breeding season, and calculated fragmentation metrics of forest structural complexity, patch size and isolation. We constructed a maximum likelihood phylogeny using the combined analysis of two mitochondrial genes and three nuclear markers and measured the influence of the fragmentation metrics on six measures of phylogenetic diversity. Our results indicated that the avian community was variously affected by anthropogenic forest fragmentation, with the different metrics of phylogenetic diversity responding with no definitive overall pattern. However, forest structural complexity emerged as an important metric explaining phylogenetic structuring. While the avian community’s phylogenetic diversity displayed resilience to anthropogenic fragmentation, previous research showed a reduction in functional diversity along the fragmentation gradient. Therefore, we recommend studies that especially aim to guide conservation management, incorporate both phylogenetic and functional diversity measures to sufficiently interrogate communities’ resilience to the threats under investigation.

Mapping Amazon Forest Productivity by Fusing GEDI Lidar Waveforms with an Individual-Based Forest Model

The Amazon rainforest plays an important role in the global carbon cycle. However, due to its structural complexity, current estimates of its carbon dynamics are very imprecise. The aim of this study was to determine the forest productivity and carbon balance of the Amazon, particularly considering the role of canopy height complexity. Recent satellite missions have measured canopy height variability in great detail over large areas. Forest models are able to transform these measurements into carbon dynamics. For this purpose, about 110 million lidar waveforms from NASA’s GEDI mission (footprint diameters of ~25 m each) were analyzed over the entire Amazon ecoregion and then integrated into the forest model FORMIND. With this model–data fusion, we found that the total gross primary productivity (GPP) of the Amazon rainforest was 11.4 Pg C a−1 (average: 21.1 Mg C ha−1 a−1) with lowest values in the Arc of Deforestation region. For old-growth forests, the GPP varied between 15 and 45 Mg C ha−1 a−1. At the same time, we found a correlation between the canopy height complexity and GPP of old-growth forests. Forest productivity was found to be higher (between 25 and 45 Mg C ha−1 a−1) when canopy height complexity was low and lower (10–25 Mg C ha−1 a−1) when canopy height complexity was high. Furthermore, the net ecosystem exchange (NEE) of the Amazon rainforest was determined. The total carbon balance of the Amazon ecoregion was found to be −0.1 Pg C a−1, with the highest values in the Amazon Basin between both the Rio Negro and Solimões rivers. This model–data fusion reassessed the carbon uptake of the Amazon rainforest based on the latest canopy structure measurements provided by the GEDI mission in combination with a forest model and found a neutral carbon balance. This knowledge may be critical for the determination of global carbon emission limits to mitigate global warming.

Niche lability mitigates the impact of invasion but not urbanization.

Native species can coexist with invasive congeners by partitioning niche space; however, impacts from invasive species often occur alongside other disturbances. Native species' responses to the interactions of multiple disturbances remain poorly understood. Here we study the impacts of urbanization and an invasive congener on a native species. Using abundance (catch-per-unit effort) and vertical distribution of native green anoles (Anolis carolinensis) and invasive brown anoles (Anolis sagrei) across a gradient of natural-to-urban forests, we ask if niche shifting (lability) is occurring, and if it can mitigate impacts from one or both disturbances. We use generalized linear models to relate species abundances across the landscape to urbanization, forest structural complexity, and congener abundances (i.e., A. sagrei); and test for an interaction between urbanization and congener abundance. Our data show that A. sagrei presence results in a 17-fold upward shift in vertical niche of A. carolinensis-an 8.3m shift in median perch height, and models reveal urbanization also drives an increase in A. carolinensis perch height. A. carolinensis and A. sagrei abundances negatively and positively correlate with urbanization, respectively, and neither species' abundance correlate with congener abundance. Despite a positive correlation between A. sagrei abundance and urbanization, our results do not show evidence of this interaction affecting A. carolinensis. Instead, niche lability appears to enable the native species to mitigate the impact of one driver of decline (invasive competition) while our data suggest it declines with the second (urbanization).

Accelerating the development of structural complexity: lidar analysis supports restoration as a tool in coastal Pacific Northwest forests

A century of land-use changes and intensive plantation-style forestry in the Pacific Northwest have resulted in a substantial deficit of structurally complex late-successional forests, which serve key ecological and social functions. Restoration treatments can theoretically accelerate the development of structural complexity in simplified forest stands, but our understanding of the short-term effects of such treatments on forest structure and gap patterns is still emerging. In this study, we used a bi-temporal airborne lidar dataset to assess short-term (10 years post-treatment) effects of restoration treatments on the structural complexity of forest stands in the Ellsworth Creek Preserve in southwestern Washington. We also compared forest structure and gap patterns between treated stands and existing late-successional areas within the preserve. Restoration treatments in older stands (60- to 80-years old) resulted in a greater increase in the mean and variability of both vertical and horizontal structural complexity and a considerable increase in the density of canopy gaps, indicating accelerated development towards increased structural complexity. Treatments in the younger stands (20- to 30-years old) primarily accelerated the development of vertical canopy complexity. Older treated stands within the preserve will require natural growth in canopy height, increased regeneration density, and filling in and diversification of canopy gaps to reach the structural complexity of late-successional forests in the preserve, but accelerating these developments may require additional restoration-focused treatments. Forest managers in the region should consider restoration treatments as a viable option for improving short-term structural complexity in simplified forest stands. We also encourage long-term management and monitoring plans that continue to address the deficit of late-successional forests in the region.

A palaeovegetation and diatom record of tropical montane forest fire, vegetation and hydroseral changes on Mount Kenya from 27,000–16,500 cal yr BP

Fire is an important ecological disturbance in moist tropical forests influencing vegetation composition and structure. Contemporary and historical records of forest fires in mountain forests of Kenya are limited to the past decades and have a strong anthropogenic influence for ignition patterns and fire suppression activities. Palaeoenvironmental geoarchives provide the temporal depth to investigate long-term (multidecadal-to-millennial) changes in fire activity. Here we use a sediment record from the Rumuiku wetland, located in a volcanic crater on the eastern flank of Mount Kenya that was radiocarbon dated and analysed for diatom, pollen and charcoal subfossils to produce a highly resolved time series of local hydroclimatic change, vegetation, and fire; respectively. This study focuses on the time during and following the global Last Glacial Maximum, a time of rapid warming and changing regional hydroclimate with relatively stable atmospheric CO2 and not yet intensive anthropogenic modification of ecosystems. Charcoal and pollen data support associated changes in vegetation-fire centred around 21,500 cal yr BP when Afromontane forests with predominant abundances of Juniperus, Podocarpus and other montane forest trees changed to Hagenia-dominated forests that are relatively more open and adapted to burn more frequently but with less intense fires.These transitions in ecosystem composition, distribution and structure support the important role of fire in driving and maintaining forest composition in the watershed and contributing to the spatial complexity of forests around the mountain. These changes in composition, structure and biomass occurred during a time of rapid Late Pleistocene climate warming, regional hydroclimatic drying, and slowly rising atmospheric CO2 from 27,000 to 16,500 cal yr BP, during and following the conditions of the global Last Glacial Maximum. Temperature, hydroclimate and atmospheric CO2 are well-known drivers of montane vegetation change in the tropics and the role of fire is shown here to be a contributing driver to the spatial heterogeneity of forest patches at long time scales. Vegetation modelling at spatial scales relevant to land management and conservation should include retrospective evidence of the range of drivers of ecological disturbance regimes.

Particularities of Forest Dynamics Using Higuchi Dimension. Parâng Mountains as a Case Study

The legal or illegal losses and the natural disturbance regime of forest areas in Romania generate major imbalances in territorial systems. The main purpose of the current research was to examine the dynamics of the complexity of forests under the influence of forest loss but also to compare the applicability of Higuchi dimension. In this study, two fractal algorithms, Higuchi 1D (H1D) and Higuchi 2D (H2D), were used to determine qualitative and quantitative aspects based on images obtained from a Geographic Information System (GIS) database. The H1D analysis showed that the impact of forest loss has led to increased fragmentation of the forests, generating a continuous increase in the complexity of forest areas. The H2D analysis identified the complexity of forest morphology by the relationship between each pixel and the neighboring pixels from analyzed images, which allowed us to highlight the local characteristics of the forest loss. The H1D and H2D methods showed that they have the speed and simplicity required for forest loss analysis. Using this methodology complementary to GIS analyses, a relevant status of how forest loss occurred and their impact on tree-cover dynamics was obtained.

The Driving Factors of Subtropical Mature Forest Productivity: Stand Structure Matters

Forest productivity (increment of above-ground biomass) is determined by biodiversity but also by stand structure attributes. However, the relative strengths of these drivers in determining productivity remain controversial in subtropical forests. In this study, we analyzed a tree growth data from 500 plots with in a 20 ha mature subtropical forest in eastern China. We used spatial simultaneous autoregressive error models to examine the effects of diversity variables (species richness, evenness, and composition), stand structural attributes (stand density, tree size range and diversity), environmental factors (topography and soil), and initial above-ground biomass (AGB) on productivity. We also applied structural equation models to quantify the relative importance of diversity, stand structure, environmental factors, and initial AGB in determining forest productivity. Our results showed that stand structure together with diversity and initial AGB governed forest productivity. Tree size diversity (DBH Shannon’s diversity index) had the largest positive effect on forest productivity. These results provide new evidence that structural explanatory variables have greater contributions to productivity for mature subtropical forests, strongly supporting the niche complementarity hypothesis. Our work highlights the importance of tree size diversity in promoting high forest productivity, and suggests that regulating and conserving complexity of forest stand structure should be among the most important goals in subtropical forest management.

Influence of Forest Fragment Composition and Structure on Ground-Dwelling Arthropod Communities

Habitat fragmentation is the process of reducing habitat area while increasing the number and isolation of habitat patches. Although much of Indiana's land area was historically covered with contiguous forests, remaining forests are now heavily fragmented. This is especially true in northeastern Indiana where agriculture is the dominant land use cover type. Loss of functional forests in northeast Indiana could lead to a loss of biodiversity at a regional scale. Ground-dwelling arthropods have been used frequently as biological indicator taxa of forest health. We characterized 10 typical northeast Indiana forest plant communities and inventoried ground-dwelling arthropod communities within those forests. Plant community and environmental heterogeneity within forests were used to assess forest complexity, and ground-dwelling arthropod communities were compared to forest environmental characteristics. Our forest comparisons revealed plant community and structural heterogeneity differences. While overstory and understory diversity, compositional heterogeneity, and litter depth did have influence on arthropod communities' relative dissimilarities in nonmetric multidimensional scaling ordination plots, those communities were similar across all forest patches. However, those same environmental variables did not have direct influence on overall arthropod abundance, richness, or diversity. Even though differences did occur in forest structure and composition, arthropod communities had high similarity values, especially in August. As the forests in the region are similar in type and structure, between-forest comparisons of arthropod communities showed corresponding similarities in composition, abundance, richness, and diversity.

Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon–Waver (H’) and Simpson’s diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H’and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H’and D, the RMSE was 5.2–10% with a bias between −1.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2–27.6% and the bias between −12.4% and −0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data.

Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management

The ecological value of tropical forests in water conservation district has been of great interest because of their rich vegetation types and higher biomass density than any other land cover types, it is urgent to evaluate the ecological value of tropical forests in water conservation district. However, the monitoring of tropical forests in water conservation district is faced with many problems, such as high forest density, complexity and diversity of the forest structure, complex topography and climate conditions, and the difficulty of access for investigators. In order to solve the above difficulties, this study combined 3D point cloud reconstruction based on Unmanned Aerial Vehicle - Structure from Motion (UAV-SfM) technology with forest type classification based on the Convolutional Neural Network (CNN) method, combined with a small amount of forest permanent sample plot survey data, to accurately evaluate the forest biomass distribution and forest biodiversity in water conservation district. The results show that the overall classification accuracy of the 20 forest types in water conservation district based on the CNN method is 0.61, the overall Kappa coefficient is 0.59, and the conditional Kappa coefficient is concentrated in the range of 0.43–0.85. The Root Mean Square Error (RMSE) of the plane measurement of UAV-SfM technology is 0.432 m, and the RMSE of the elevation measurement is 0.989 m, the effect of this UAV technology in tropical forest monitoring is superior. Using the techniques mentioned above, this study can effectively and accurately monitor and evaluate the biomass distribution and biodiversity of tropical forests in the water conservation district. Based on the precision forest ecological monitoring data, this study can develop a scientific and reasonable sustainable forest management plan for the water conservation district according to the distribution of forest biomass and biodiversity. The combination of UAV-SfM technology and the CNN method is an innovative attempt, and the integration of UAV and artificial intelligence technology solves practical problems faced by sustainable forest management. UAV and artificial intelligence will also provide an important foundation for forest ecological environment sustainability assessment research.

Understory plant diversity under variable overstory cover in Mediterranean forests at different spatial scales

Mediterranean forest ecosystems are of high conservation value and therefore increasingly managed to restore and promote their natural biodiversity and complexity. The present study aimed to assess plant species diversity of East-Mediterranean conifer forests as affected by overstory cover manipulation at multiple spatial scales. Understanding this interaction in water-limited forest ecosystems is particularly challenging as overstory-understory relations involve both water and light constrains. The experiment was conducted in a mature (40y) planted Pinus halepensis forest in the Jerusalem Mountains of Israel. Understory plant community structure was monitored for five years in sixteen experimental plots (0.5 ha) representing a range of overstory cover levels (leaf area index-LAI = 0–4 m2m−2) created by tree thinning. Light and water availability in the forest understory were monitored using fisheye camera (irradiance), predawn leaf water potential (PLWP) of dominant species and soil water content (SWC) measurements. Plant diversity was measured at increasing spatial scales (grain sizes, 0.01–400 m2), using hierarchical nested sampling. Irradiance and SWC increased with decreasing LAI while PLWP was not affected. Stand-level understory richness (α diversity) increased linearly with decreasing LAI, indicating a consistent, positive resource availability-diversity relationship. The results pointed towards strong dependency of species richness on light availability as compared to its dependency on water availability that was less definite. Understory species composition varied across LAI levels (β diversity at the forest level) indicating species turnover, mainly between annual herbs benefiting from reduced overstory cover vs. woody species benefiting from higher cover. The relationships between overstory cover and understory richness and composition were rank-invariant across grain sizes. These relationships were stronger at the larger (10, 100, 400 m2) compared to the smaller (0.01, 0.1, 1 m2) grain sizes. The 10-m2 grain-size, corresponding to the crown projection area of individual trees, showed the strongest relationship. Observed patterns of overstory cover–understory richness suggest that resource availability, rather than spatial resource heterogeneity, was the main factor shaping understory diversity. Furthermore, grain-size effects on this relationship support “environmental filtering” rather than “resource density” as the main mechanism through which reduced overstory cover promoted understory diversity.'Synthesis and applications’: Our findings demonstrate the importance of overstory cover as a key factor determining plant diversity in water-limited East-Mediterranean forests. We highlight the important role of spatial heterogeneity of overstory cover at both the stand (intra-plot) and forest (inter-plot) scales and support dynamic variable thinning as a management strategy to enhance forest biodiversity and complexity.

Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification

McArthur's foliage height diversity (FHD) has been the gold standard in the determination of structural complexity of forests characterized by LiDAR vertical height profiles. It is based on Shannon's entropy index, which was originally designed to describe evenness in abundances among qualitative typologies, and thus the calculation of FHD involves subjective layering steps which are essentially unnatural to describe a continuous variable (X) such as height. In this contribution we aim to provide a mathematical framework for determining maximum entropy in 3D remote sensing datasets based on the Gini Coefficient of theoretical continuous distributions, intended to replace FHD as entropy measure in vertical profiles of LiDAR heights (1D, X), with extensions to variables expressing dimensions of higher order (2D or 3D, Z ∝ X2 or X3). Then we apply this framework to Boreal forests in Finland to describe landscape heterogeneity with the intention to improve the modelling of forest aboveground biomass (AGB), hypothesizing that LiDAR models of AGB should essentially be different in areas of differing structural characteristics. We carried out a pre-stratification of LiDAR data collected in 2012 using simple rules applied to the L-skewness (Lskew) and L-coefficient of variation of LiDAR echo heights (Lcv; equivalent to the Gini coefficient, GCH), determining a new threshold at GCH = 0.33 as a consequence of the newly developed mathematical proofs. We observed only moderate improvements in terms of model accuracies: RMSDs reduced from 41.7% to 38.9 or 37.0%. More remarkably, we identified critical differences in the metrics selected at each stratum, which is useful to understand what predictor variables are more important for estimating AGB at each area of a forest. We observed that higher LiDAR height percentiles are more relevant at open canopies and heterogeneous forests, whereas closed canopies in homogeneous forests obtain most accurate predictions from a combination of cover metrics and percentiles around the median. Without stratification, the overall model would neglect explained variability in the structural types of lower occurrence, and predictions from a model influenced by structural types of higher occurrence would be biased at those areas. These results are thus useful in terms of improving our understanding on the relationships underlying LiDAR-AGB models.

Composition and Specialization of the Lichen Functional Traits in a Primeval Forest—Does Ecosystem Organization Level Matter?

Current trends emphasize the importance of the examination of the functional composition of lichens, which may provide information on the species realized niche diversity and community assembly processes, thus enabling one to understand the specific adaptations of lichens and their interaction with the environment. We analyzed the distribution and specialization of diverse morphological, anatomical and chemical (lichen secondary metabolites) traits in lichen communities in a close-to-natural forest of lowland Europe. We considered these traits in relation to three levels of forest ecosystem organization: forest communities, phorophyte species and substrates, in order to recognize the specialization of functional traits to different levels of the forest complexity. Traits related to the sexual reproduction of mycobionts (i.e., ascomata types: lecanoroid apothecia, lecideoid apothecia, arthonioid apothecia, lirellate apothecia, stalked apothecia and perithecia) and asexual reproduction of mycobionts (pycnidia, hyphophores and sporodochia) demonstrated the highest specialization to type of substrate, tree species and forest community. Thallus type (foliose, fruticose, crustose and leprose thalli), ascospore dark pigmentation and asexual reproduction by lichenized diaspores (soredia and isidia) revealed the lowest specialization to tree species and substrate, as well as to forest community. Results indicate that lichen functional trait assemblage distribution should not only be considered at the level of differences in the internal structure of the analyzed forest communities (e.g., higher number of diverse substrates or tree species) but also studied in relation to specific habitat conditions (insolation, moisture, temperature, eutrophication) that are characteristic of a particular forest community. Our work contributes to the understanding of the role of the forest structure in shaping lichen functional trait composition, as well as enhancing our knowledge on community assembly rules of lichen species.

Quantifying Understory Complexity in Unmanaged Forests Using TLS and Identifying Some of Its Major Drivers

The structural complexity of the understory layer of forests or shrub layer vegetation in open shrublands affects many ecosystem functions and services provided by these ecosystems. We investigated how the basal area of the overstory layer, annual and seasonal precipitation, annual mean temperature, as well as light availability affect the structural complexity of the understory layer along a gradient from closed forests to open shrubland with only scattered trees. Using terrestrial laser scanning data and the understory complexity index (UCI), we measured the structural complexity of sites across a wide range of precipitation and temperature, also covering a gradient in light availability and basal area. We found significant relationships between the UCI and tree basal area as well as canopy openness. Structural equation models (SEMs) confirmed significant direct effects of seasonal precipitation on the UCI without mediation through basal area or canopy openness. However, annual precipitation and temperature effects on the UCI are mediated through canopy openness and basal area, respectively. Understory complexity is, despite clear dependencies on the available light and overall stand density, significantly and directly driven by climatic parameters, particularly the amount of precipitation during the driest month.

Macroecological patterns of forest structure and allometric scaling in mangrove forests

AbstractAimMangrove wetlands span broad geographical gradients, resulting in functionally diverse tree communities. We asked whether latitudinal variation, allometric scaling relationships and species composition influence mangrove forest structure and biomass allocation across biogeographical regions and distinct coastal morphologies.LocationGlobal.Time periodPresent.Major taxa studiedMangrove ecosystems.MethodsWe built the largest field‐based dataset on mangrove forest structure and biomass to date (c. 2,800 plots from 67 countries) to address macroecological questions pertaining to structural and functional diversity of mangroves spanning biogeographical and coastal morphology gradients. We used frequentist inference statistics and machine learning models to determine environmental drivers that control biomass allocation within and across mangrove communities globally.ResultsAllometric scaling relationships and forest structural complexity were consistent across biogeographical and coastal morphology gradients, suggesting that mangrove biomass is controlled by regional forcings rather than by latitude or species composition. For instance, nearly 40% of the global variation in biomass was explained by regional climate and hydroperiod, revealing nonlinear thresholds that control biomass accumulation across broad geographical gradients. Furthermore, we found that ecosystem‐level carbon stocks (average 401 ± 48 MgC/ha, covering biomass and the top 1 m of soil) varied little across diverse coastal morphologies, reflecting regional bottom‐up geomorphic controls that shape global patterns in mangrove biomass apportioning.Main conclusionsOur findings reconcile views of wetland and terrestrial forest macroecology. Similarities in stand structural complexity and cross‐site size–density relationships across multiscale environmental gradients show that resource allocation in mangrove ecosystems is independent of tree size and invariant to species composition or latitude. Mangroves follow a universal fractal‐based scaling relationship that describes biomass allocation for several other terrestrial tree‐dominated communities. Understanding how mangroves adhere to these universal allometric rules can improve our ability to account for biomass apportioning and carbon stocks in response to broad geographical gradients.