•Evaluating methods to derive stand descriptions from large-scale sampling data.•Multi-scale approach to improve initialisation of dynamic forest models.•Simultaneous parameter prediction method best ...to predict tree diameter distributions.•Random Forest approach best to predict tree species composition.
Most strategic and operational forest management decisions are taken based on stand-level information, and quantitative models of forest dynamics are key for developing sustainable management strategies. However, data on forest stands for the initialisation of such models that are representative at large spatial scales, e.g., countries or ecoregions, are often lacking. National Forest Inventories (NFIs) provide forest data from small sample plots at large spatial scales, yet deriving full stand information based on such data is challenging. Here, we evaluate seven methods of varying complexity for deriving quantitative stand descriptions based on sample data as provided by the Swiss NFI. We selected 271 extensively measured Swiss forests stands with unimodal diameter distributions, classified them as beech- vs. spruce-dominated in five development stages and randomly placed a small sized sample plot in each stand using the Swiss NFI sampling design (i.e., a circular plot of 500 m2). Seven modelling approaches were used to derive diameter distributions and species-specific stem numbers (i.e., tree species composition) from the sample data that are representative for a particular stand (local scale) and for stand types in general (generalised scale). The prediction performance of the modelling approaches was evaluated using 100 random samples per stand to calculate prediction errors. Generalised even-aged diameter distributions were best predicted by the simultaneous parameter prediction method (PPM), i.e. a combined three-step regression approach, with on average 1.3 to 2.5 times lower prediction errors compared to the simple pooling of diameter samples. However, uneven-aged diameter distributions were best predicted by pooling. At the local scale, the simultaneous PPM performed best for data from sample plots with fewer than 17 to 19 trees across all development stages. Prediction performance of the PPMs increased for structurally and spatially diverse local stands with positively skewed diameter distributions. A Random Forest approach was most suitable for predicting species composition at both the generalised and the local scale. Our study evaluates the strengths and weaknesses of methods to model stands based on data from small sample plots. We emphasise terminological pitfalls by consequently distinguishing local accuracy and generalised representativity of the stand descriptions. We demonstrate the feasibility of deriving locally accurate stands using data from small forest sample plots and evaluate the derivation of generalised stands representative at large regions. At both scales, our developments contribute to an improved initialisation of forest models and thus to a more realistic modelling of forest development under future boundary conditions.
Abstract
Key message
Authors have analyzed the possible correlation between measurements/indicators of forest structure and species richness of many taxonomic or functional groups over three regions ...of Germany. Results show the potential to use structural attributes as a surrogate for species richness of most of the analyzed taxonomic and functional groups. This information can be transferred to large-scale forest inventories to support biodiversity monitoring.
Context
We are currently facing a dramatic loss in biodiversity worldwide and this initiated many monitoring programs aiming at documenting further trends. However, monitoring species diversity directly is very resource demanding, in particular in highly diverse forest ecosystems.
Aims
We investigated whether variables applied in an index of stand structural diversity, which was developed based on forest attributes assessed in the German National Forest Inventory, can be calibrated against richness of forest-dwelling species within a wide range of taxonomic and functional groups.
Methods
We used information on forest structure and species richness that has been comprehensively assessed on 150 forest plots of the German biodiversity exploratories project, comprising a large range of management intensities in three regions. We tested, whether the forest structure index calculated for these forest plots well correlate with the number of species across 29 taxonomic and functional groups, assuming that the structural attributes applied in the index represent their habitat requirements.
Results
The strength of correlations between the structural variables applied in the index and number of species within taxonomic or functional groups was highly variable. For some groups such as Aves, Formicidae or vascular plants, structural variables had a high explanatory power for species richness across forest types. Species richness in other taxonomic and functional groups (e.g., soil and root-associated fungi) was not explained by individual structural attributes of the index. Results indicate that some taxonomic and functional groups depend on a high structural diversity, whereas others seem to be insensitive to it or even prefer structurally poor stands.
Conclusion
Therefore, combinations of forest stands with different degrees of structural diversity most likely optimize taxonomic diversity at the landscape level. Our results can support biodiversity monitoring through quantification of forest structure in large-scale forest inventories. Changes in structural variables over inventory periods can indicate changes in habitat quality for individual taxonomic groups and thus points towards national forest inventories being an effective tool to detect unintended effects of changes in forest management on biodiversity.
•Forest biomass and productivity are correlated to tree density and NDVI.•Species models differ on the effect of soil factors on biomass and productivity.•Aridity negatively affects forest biomass ...and productivity.•Projections of increasing aridity show a decrease on forest biomass and productivity.
One of the main challenges under global warming is understanding and predicting the effects of increased aridity on the carbon sink role of forests, particularly in Mediterranean regions. Forest inventories monitor the real state of the forest at a high temporal and financial cost. Cloud computing tools and high spatio-temporal resolution datasets generate fast and low-cost remote sensing data. Our objective is to understand the underlying variables explaining carbon storage (aboveground biomass) and forest productivity of Mediterranean forests using remote and in field-based variables and predict expected future trends. Then, we quantify the potential effects of a hypothetical increase in aridity under climate change on aboveground biomass and forest productivity. We included remote sensing indices (NDVI), abiotic factors (climate, soil and topography) and biotic factors (forest structure) as key variables of forest biomass and productivity in a large and heterogeneous Mediterranean region (Andalusia, southern Spain). We used around 7000 forest plots from the second and the third Spanish National Forest Inventory (1995 and 2006) considering the eight most abundant species (Olea europaea, Pinus pinea, P. pinaster, P. halepensis, P. nigra, P. sylvestris, Quercus ilex subsp ballota, and Q. suber). The variance explained by the models ranged from 25% in Q. ilex forests to 65% in P. sylvestris forests. Aridity affected all-species and Quercus biomass and most productivity models. NDVI and tree density had a strong positive effect on forest biomass and productivity with a significant interaction effect in all-species models, whereas aridity had a negative effect on both. The predicted increase in aridity under future climate change scenarios could seriously reduce forest biomass by 18% and productivity by 16%. Our study suggests that aridity is a key factor determining forest biomass and productivity in Mediterranean forests, that could potentially lead to reductions of their carbon sink role.
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Abstract
Key message
This paper proposes a methodology that could be considered as a base for a harmonized protocol for stem-quality reporting in Europe while conducting National Forest Inventories, ...in order to cost-efficiently obtain a visual wood quality proxy. The importance of the variables selected, the limitations identified, and some improvements to the methodology are suggested. Forest areas with better wood quality, which in turn it would be useful for breeding programs, can be easily detected.
Context
The establishment of harmonized standards or indicators that allow us to determine the quality of the wood present in a forest prior to its exploitation has long been demanded by the European forestry sector, although agreed methodologies for the evaluation of wood quality in standing trees, which is one of the sector’s most urgent requirements, have not, as yet, been implemented.
Aims
To develop a protocol that visually characterizes wood quality on standing trees in a cost-effective way for the National Forest Inventory (NFI). After some improvements, it can be considered as a base for a European harmonized protocol.
Methods
In this article, we analyze the implementation, in the NFI, of a visual wood-quality assessment methodology in forests of Central Spain based on the different European standards as well as on research papers addressing this issue.
Results
The silvicultural practices employed are of the utmost importance to obtain the best wood quality, regardless of the species. Several areas with higher wood quality were identified as well as areas most affected by specific pests in the studied region. The impact of the variables measured (e.g., branchiness, crookedness, maximum branch diameter) is discussed.
Conclusion
It is feasible to estimate a proxy for wood quality on standing trees in the NFI. Furthermore, after studying the inventory data provided, several enhancements are proposed, not only to improve wood-quality estimates but also to optimize fieldwork costs. Harmonizing NFIs to assess and map European standing wood quality can be achieved.
•Modern aspen forest management guides are based on data from nearly a century ago.•Current atmospheric CO2, climate, and management objectives changed over a century.•Mismatched data for guides and ...forest conditions reduces forest health and management.•Aspen stocking tables recalculated with modern data show that guides are outdated.•New management guides should be dynamic to empower adaptive management.
Since the development of contemporary stocking techniques a century ago, the combination of climatic, atmospheric, financial, and social factors that determine forest management strategies have changed, altering aspen stand dynamics in the western Great Lakes, USA. Despite this, aspen management is still informed by 1970s management guides that are based on 1920s inventories; hence, a century exists between the data that underlie current management guidelines and current stand conditions. We hypothesized that current aspen stands may support higher stocking and height growth than nearly a century ago at relatively similar age and site indices, due to increased atmospheric CO2 concentrations and fertilization, intensive coppice harvests, and other factors. To explore this question, we compared historic aspen observations with comparable contemporary data from the USDA Forest Service’s Forest Inventory and Analysis program. The results show increased stand stocking levels as well as increased height growth of aspen throughout the region over the historic inventory data. Although other controlled experimental studies support the hypothesis of increased carbon fertilization altering aspen size-density relationships, our study is the first to examine an empirical application to forest management guides. Our results suggest a comprehensive reevaluation of aspen growth dynamics under contemporary environmental conditions is warranted. We highlight the need to assess the value of current stocking standards in an era of increasingly variable environmental conditions and to reimagine a more dynamic, responsive, and predictive approach to guide forest management for future application as global change may accelerate.
•MSDR and SDImax estimations are significantly influenced by climate.•All selected climate-dependent MSDRs improved SDImax estimations over the basic MSDRs.•Seasonal climatic variables better explain ...SDImax variations than general climatic indexes.•Spring and summer climate changes are key drivers affecting the MSDR and SDImax.•Lower values of SDImax are linked to warmer and drier conditions.
Climate change projections for the Mediterranean basin predict a continuous increase in extreme drought and heat episodes, which will affect forest dynamics, structure and composition. Understanding how climate influences the maximum size-density relationship (MSDR) is therefore critical to designing adaptive silvicultural guidelines based on the potential stand carrying capacity of tree species. With this aim, data from the Third Spanish National Forest Inventory (3NFI) and WorldClim databases were used to analyze climate-related variations of the maximum stand carrying capacity for 15 species from the Pinus, Fagus and Quercus genera. First, basic MSDR were fitted using linear quantile regression and observed size-density data from monospecific 3NFI plots. Reference values for maximum stocking, expressed in terms of the Maximum Stand Density Index (SDImax), were estimated by species. Then, climate-dependent MSDR models including 35 annual and seasonal climatic variables were fitted. The best climate-dependent models, based on the Akaike Information Criteria (AIC) index, were used to determine the climatic drivers affecting MSDR, to analyze general and species-specific patterns and to quantify the impact of climate on maximum stand carrying capacity. The results showed that all the selected climate-dependent models improved the goodness of fit over the basic models. Among the climatic variables, spring and summer maximum temperatures were found to be key drivers affecting MSDR for the species studied. A common trend was also found across species, linking warmer and drier conditions to smaller SDImax values. Based on projected climate scenarios, this suggests potential reductions in maximum stocking for these species. In this study, a new index was proposed, the Q index, for evaluating the impact of climate on maximum stand carrying capacity. Our findings highlight the importance of using specific climatic variables to better characterize how they affect MSDR. The models presented in this study will allow us to better explain interactions between climate and MSDR while also providing more precise estimates concerning maximum stocking for different Mediterranean coniferous and broadleaf tree species.
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•Predicting ingrowth is important for estimating the future development of forests.•Ingrowth is a stochastic process with a zero-inflated distribution.•The number of ingrowth trees ...was predicted better by ZIP than by NB or ZINB.•Diameter of ingrowth trees was adequately predicted with the Weibull distribution.•Species were sufficiently ascribed to ingrowth trees by multinomial regression.
Accurate and representative prediction of ingrowth is essential for modeling forest development. Besides the number of ingrowth trees, the basic tree attributes diameter and species are also important. In this study, these three characteristics were modeled based on data from the Swiss National Forest Inventory (NFI). The study covered large gradients of stand conditions and climate variables, making the models suitable to predict ingrowth under climate change.
As the number of ingrowth trees per plot included more zeros than is expected for a Poisson distribution, we used three alternative probability distributions: zero-inflated Poisson distribution (ZIP), negative binomial distribution (NB) and zero-inflated negative binomial distribution (ZINB). Models with each of the three variants were fitted with and without random effects, resulting in six different model types. Model selection was performed backward using the BIC criterion. Of the final models, ZIP showed the best predictions of independently observed number of ingrowth trees.
Our results indicate that the number of ingrowth trees strongly depended on the development stage of forests and on stand basal area, while temperature and precipitation, nitrogen deposition and water holding capacity each had a lower but still significant and plausible effect. The Weibull function was used to describe the probability distribution of the diameter of ingrowth trees and parameters were estimated using the Likelihood approach. The diameter of ingrowth trees was larger where there was a better site index and decreased with increasing stand density. Further, twelve species groups of ingrowth trees were fitted with a multinomial regression approach and showed clear dependence on climate: the probability of spruce and larch ingrowth clearly decreased with increasing temperature, whilst all other tree species profited from warmer conditions. The probability of fir, beech and ash ingrowth increased with increasing basal area, demonstrating the relevance of shade tolerance. The most important variable for predicting the species of ingrowth was the leading tree species group in a plot.
Downed woody material (DWM) is a unique part of the forest carbon cycle serving as a pool between living biomass and subsequent atmospheric emission or transference to other forest pools. Thus, DWM ...is an individually defined pool in national greenhouse gas inventories. The diversity of DWM carbon drivers (e.g., decay, tree mortality, or wildfire) and associated high spatial variability make this a difficult-to-predict component of forest ecosystems. Using the now fully established nationwide inventory of DWM across the United States (US), we developed models, which substantially improved predictions of stand-level DWM carbon density relative to the current national-reporting model (‘previous’ model, here). The previous model was developed from published DWM carbon densities prior to the NFI DWM inventory. Those predictions were tested using NFI DWM carbon densities resulting in a poor fit to the data (coefficient of determination, or R2 = 0.03).
We present new random forest (RF) and stochastic gradient boosted (SGB) regression models to prediction DWM carbon density on all NFI plots and spatially on all forest land pixels. We evaluated various biotic and abiotic regression predictors, and the most important were standing dead trees, long-term annual precipitation, and long-term maximum summer temperature. A RF model scored best for expanding predictions to NFI plots (R2 = 0.31), while an SGB model was identified for DWM carbon predictions based on purely spatial data (i.e., NFI-plot-independent, with R2 = 0.23). The new RF model predicts conterminous US DWM carbon stocks to be 15% lower than the previous model and 2% higher than NFI data expanded according to inventory design-based inference. The new NFI data-driven models not only improve the predictions of DWM carbon density on all plots, they also provide flexibility in extending these predictions beyond the NFI to make spatially explicit and spatially continuous estimates of DWM carbon on all forest land in the US.
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•New regression models developed to predict forest downed woody material (DWM) stock.•National forest inventory (NFI) data used to train non-parametric regression models.•New model predictions of DWM carbon stocks outperformed previous model predictions.•Important predictors: dead trees, long-term precipitation and summer temperature•Updated predictions show a 15% decrease in DWM carbon stocks over Continental US.
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•Human pressure is the main cause of LAI decline inside forest fragments.•Distance to the forest edge directly affects LAI.•Rainfall and topography play a secondary role, shaping ...human pressure.•Management for LAI should aim to prevent fragmentation and increasing forest fragment size.
The Atlantic Forest, a global biodiversity hotspot, has changed dramatically due to land use pressures causing deforestation, degradation, and forest fragmentation. A major challenge is to understand and potentially mitigate the consequences of these changes, for the capacity of forests to deliver essential environmental services to rural areas. Here, we focus on unraveling the mechanisms underpinning spatial variation in forest leaf area index. Forest leaf area index can be used as an environmental indicator that controls key forest functions underlying environmental services and is also expected to respond to land use change. Specifically, we use Structural Equation Modelling to determine the direct and indirect pathways that link environmental drivers to canopy leaf area index (LAI) variation across forest types in the Atlantic Forest in Southern Brazil. We sampled 240 sample units (each 4000 m2), from a systematic and permanent forest inventory set which covers the State of Santa Catarina in a 10 km × 10 km grid, using hemispherical photographs. Environmental variables were extracted for each sample unit, including climatic and topographic data as well as indicators of anthropogenic pressure. Our results showed that forest types differed in their leaf area index (but not all of them) and that forest canopies show complex responses to environmental drivers, encompassing direct and indirect pathways. A major pathway was the positive effect of ‘Distance to city’ on the ‘Percentage of cropland in the matrix’. This led to a decline in the distance of the sample unit to the forest edge, indirectly reducing LAI, presumably because of elevated tree mortality at the forest edge. ‘Terrain steepness’ and ‘Rainfall in the driest month’ independently affected the ‘Percentage of cropland in the matrix’ and the ‘Distance to forest edge’. Halting forest fragmentation and increasing fragment size by landscape planning will mitigate these anthropogenic LAI declines. This can be achieved with a combination of legal and market mechanisms, like enforcement of the Brazilian Forest Act regulation on buffer zones around water bodies and steep slopes, landscape planning, and payment for environmental services to compensate the farmers for maintaining forest cover on otherwise productive land.
Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the ...status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.
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