Forestry Decision Research Articles

High-Resolution Yield Mapping for Eucalyptus grandis—A Case Study

Yield data represent a valuable layer for supporting decision-making as they reflect crop management results. Forestry decision-makers often rely on coarse spatial resolution data (e.g., forest inventory plots) despite the availability of modern harvesters that can provide high-resolution forestry yield data. The objectives of this study were to present a method for generating high-resolution Eucalyptus grandis yield data (individual tree-level) and explore their applications, such as correlation analysis with soil attributes to aid nutrient recommendations. Two evaluations were conducted at two sites in Brazil: (a) assessing the positioning accuracy of the global navigation satellite system (GNSS) receiver positioning, and (b) analyzing the yield data and their correlation with the soil attributes. The results indicated that positioning the GNSS receiver at the harvesting head provided higher accuracy than placement at the top of the harvester cabin for individual tree-level data. Reliable yield data were generated despite the GNSS receiver’s increased susceptibility to damage when mounted on a harvest head. The linear correlation analysis between the Eucalyptus grandis yield data and soil attributes showed both negative (Clay, B, S, coarse sand, and potential acidity − H + Al) and positive correlations (K, Mg, pH-SMP, Ca, sum of bases, pH, base saturation, fine sand, total sand, and silt content). This study demonstrates the feasibility of obtaining high-resolution yield data at the individual tree-level and their correlation with soil attributes, providing valuable insights for improving forestry decision-making.

The effectiveness of public participation through forest advisory committees: Twenty years of shifting perspectives of Local Citizens Committee members in Ontario

In Canada, the most widespread approach to citizen participation in forestry decision-making is forest advisory committees. In this paper, we report on the results of a survey of forest advisory committee members that has been repeated every few years since 2001. We clustered the survey questions to create composite variables representing seven dimensions of the functioning and effectiveness of forest advisory committees: efficiency, representation, voice, decision-making process, trust in forest managers, effectiveness, and the availability, relevance, complexity and trustworthiness of information. While most committee members continue to view the process positively, after a steady improvement of committee members’ overall assessments since 2001, in the most recent survey, the proportion of those who are unsatisfied has grown. Moreover, effectiveness has consistently been assessed less favourably than the other six dimensions that we considered—dimensions upon which effectiveness presumably depends. Part of the reason is that a significant minority of committee members feel that they have little meaningful influence over forest management and that the relevance of the committees has declined. This points to the need for continual attention from policymakers to the matter of how and how much forest advisory committees are enabled to influence the direction of forest management.

Spatial and Temporal Assessment of Land Use and Land Cover in Wilpattu National Park Restricted Zone, Sri Lanka


 
 
 The Wilpattu National Park (WNP) is the country's oldest and largest National Park. Aside from the direct destruction of plant and fauna ecosystems that sustain humans with vital resources, removing forest cover throws many crucial natural processes out of balance, which could have several detrimental effects. It is still unknown how the various management alternatives will affect the economic, ecological, and social elements of sustainable forest management and planning. Sustainable development and environmental protection are aided by wise land use and resource management. Many of the farmers reside in these marginal, underproductive forest communities. Through scenario analysis and using a forestry decision support system, this study evaluated the effects of several management approaches on crucial aspects of sustainable forest management. Geographical Information System (GIS), such as participatory online mapping, GPS mapping, and satellite image classification (Remote Sensing Technology), was used. The study employs an evaluation model for land use that considers biophysical and human-related variables as part of an integrated land use assessment. Based on the classification of the images, the environment's impact has been evaluated. Forty-nine Grama Niladhari (GN) divisions are in the influenced zone in the national park. Eight sample GN divisions were selected for detailed assessment based on the area's different landscapes and ecological and cultural settings. The current situation and encroachment were studied in each sample GN division. Also, Current threats, land use, and pressure activities were studied. The land use analysis of the Wilpattu restricted zone revealed the area of encroachment. Analyses conducted in the human village clearly show that the restricted area for human settlement is being encroached upon 2019, permanent building/housing units were found in 2003 satellite image categorization within the 1.6 km restricted zone. By 2021, the number mentioned above will be 3,810 instead. The past 20 years have seen an 89% growth in housing units. Land use analysis indicates nine zones of encroachment. Zones are dominant in encroachment in 19 GN Divisions in total. The Wilpattu forest village's Mullikulam and Eluwankulam GN Divisions are the most crucial and quickly encroaching areas. The highest percentages of restricted zone, land use analysis contain Paddy cultivation, and Chena cultivation shows the second highest value. Minimum land used is utilized for abandoned Chena cultivations. The highest percentage of land use agriculture is Kudawilachiya, Dematamalagama, and Nelumvila. In addition, the lowest agricultural land use was utilized in Horawila, Thalgaswewa, Helabawewa, Mirichchukaadi, and Adiyapualwakkulam.
 
 
 
 Keywords: Sustainable, Deforestation, Forest management, GIS, Restricted zone
 
 
 
 
 

Deeply supervised network for airborne LiDAR tree classification incorporating dual attention mechanisms

ABSTRACT Accurately identifying tree species is crucial in digital forestry. Several airborne LiDAR-based classification frameworks have been proposed to facilitate work in this area, and they have achieved impressive results. These models range from the classification of characterization parameters based on feature engineering extraction to end-to-end classification based on deep learning. However, in practical applications, the loud feature noises of a single sample at varying vertical heights can cause misjudgment between intraspecific samples, thereby limiting classification accuracy. This may be exacerbated by the scanning conditions and geographic environment. To address this challenge, a deeply supervised tree classification network (DSTCN) is designed in this article, which introduced a height-intensity dual attention mechanism to deliver improved classification performance. DSTCN takes the histogram feature descriptors of each tree slice as the input vector and considers the features of each slice in combination with its height and intensity information, utilizing slice features with different information gains more effectively, and removing the accuracy limitations imposed by feature noise at varying vertical heights. Experimental results from the classification of seven tree species in a mixed forest in Baden-Württemberg, southwestern Germany indicate that DSTCN (MAF = 0.94, OA = 0.94, Kappa = 0.93, FISD = 0.02) outperforms the two commonly used methods, based on Point Net++ (MAF = 0.88, OA = 0.88, Kappa = 0.86, FISD = 0.08) and BP Net (MAF = 0.86, OA = 0.87, Kappa = 0.85, FISD = 0.06) respectively, in terms of accuracy, stability, and robustness. This method integrates feature engineering and deep network models to achieve precise and balanced classification outcomes of tree species. The simplified design enables efficient forestry decision-making and presents innovative ideas and a method for employing airborne LiDAR technology in tree species identification of large-scale multi-layer mixed stands.

Signature extension as a machine learning strategy for mapping plantation forest genera with Sentinel-2 imagery

Plantation forest inventory data such as tree genus is important for supporting forestry decisions and policymaking, but such data are traditionally collected in-field, which is time-consuming and costly. Although machine learning and remote sensing technologies have shown great potential to reduce the time and effort for mapping forest plantation genera at regional scales, they rely on training (labelled) data, which are also costly to collect over large areas. One approach to reducing the effort of training data collection is to make use of signature extension, whereby training data collected in one area is used to train and apply a machine learning model in a different area. This study aimed to evaluate the viability of training data signature extension for constructing random forest (RF) machine learning models to differentiate between acacia, eucalyptus and pine trees using Sentinel-2 imagery as input. The study was carried out over a large area (about 4920 km2) in South Africa. The study area was divided into 19 tiles of 100 × 100 km (each tile coincides with the footprint of a Sentinel-2 tile) from which three were chosen for sourcing (collecting) training data. Four experiments were conducted. In the first experiment, a fixed number (3000 per genera) of training samples were collected in the first source tile and used to build an RF model. The resulting model was then applied and assessed in all 19 tiles. This protocol was repeated in the second and third experiments using the training data collected in the other two source tiles respectively. In the final experiment, training data from all three source tiles were combined and applied to all 19 tiles. The mean overall classification accuracy of each classified tile was compared to the extension distance (i.e. the distance between the target and source tile), differences in rainfall seasonality, and variation in the mean annual temperature among tiles to gain an understanding of how signature extension efficiency is influenced by distance and environmental conditions. The results show that signature extension is viable (∼70% overall accuracies) over distances of up to 500 km, but only if the source and target tiles represent areas with similar rainfall regimes.

Emerging Signals of Change that Could Shape the Future of Forestry: a Horizon Scan

Abstract Foresight is essential for long-term planning and forward-looking decision-making in forestry, where planning horizons often span many decades. But generating foresight is challenging due to the increasing pace and complexity of change and growing uncertainty about the future. Most change that could affect or even disrupt forestry in the future originates outside of the field, such as social, technological, economic, and environmental changes. The Forest Futures Horizon Scanning project (Forest Horizons) aims to generate foresight by identifying early signals of change from outside forestry that professionals within the field may not be aware of or pay close attention to. This article draws on the database of signals of change uncovered in the Forest Horizons project and identifies fifteen key emerging changes that could help shape the long-term future of forestry. Study Implications: The long-term nature of forestry has compelled foresters to be forward-looking and plan many decades ahead. Horizon scanning is a tool that can enhance foresight in forestry by identifying emerging signals of change that could affect the field in the future. Fifteen wide-ranging early signals of change are highlighted in this article. Consideration of these emerging changes in long-term and strategic planning could help forestry decision makers prepare for change and avoid being blindsided. Horizon scanning is a useful approach to help generate essential foresight in an era of increasingly rapid, complex, and often surprising change.

From simple metrics to cervid forage: Improving predictions of ericaceous shrub biomass

Common understory vegetation species such as the ericaceous shrubs bilberry (Vaccinium myrtillus), cowberry (V. vitis-idaea) and heather (Calluna vulgaris), are key forage plant species for moose and other large herbivores, as well as fulfilling many additional ecosystem functions and services. Here we developed models to predict above-ground biomass of these ericaceous species in coniferous forests, using data on their percentage cover, height, and different stand characteristics. We also built models to understand how the aforementioned variables affect the proportion of the shrubs commonly utilized as forage by large herbivores. We found that the percentage cover of shrubs was the most important explanatory variable when predicting above-ground biomass, explaining 51%, 47% and 71% of the variation (marginal R2) in bilberry, cowberry and heather biomass, respectively. By adding ramet height to the model with percentage cover, the variation explained increased to 77% for bilberry, 75% for cowberry and 87% for heather. The best outcome for candidate models was obtained by adding stand site index and spruce basal area to the model, improving the variation explained in bilberry to 83%, to 81% for cowberry, and 91% for heather. When modelling the proportion of the shrubs commonly utilized as forage by large herbivores, stand site index and spruce basal area often played important roles. Some of the best fitting models for forage biomass explained 51% of the variation in bilberry, 59% in cowberry and 30% in heather. Site location did not have a major role in improving the variability explained in either type of model, which indicated the applicability of the models regardless of study location. Our models therefore have a high potential to be implemented in forestry decision support systems. Their inclusion should provide better large-scale estimations of forage resources, aiding forest management, and thereby taking an important step forward to determine the ecosystem carrying capacity of large herbivores.

Bioactive Molecules from Myrianthus arboreus, Acer rubrum, and Picea mariana Forest Resources.

Forest trees are the world's most important renewable natural resources in terms of their dominance among other biomasses and the diversity of molecules that they produce. Forest tree extractives include terpenes and polyphenols, widely recognized for their biological activity. These molecules are found in forest by-products, such as bark, buds, leaves, and knots, commonly ignored in forestry decisions. The present literature review focuses on in vitro experimental bioactivity from the phytochemicals of Myrianthus arboreus, Acer rubrum, and Picea mariana forest resources and by-products with potential for further nutraceutical, cosmeceutical, and pharmaceutical development. Although these forest extracts function as antioxidants in vitro and may act on signaling pathways involved in diabetes, psoriasis, inflammation, and skin aging, much still remains to be investigated before using them as therapeutic candidates, cosmetics, or functional foods. Traditional forest management systems focused on wood must evolve towards a holistic approach, allowing the use of these extractives for developing new value-added products.

Influence of Rural Out-Migration on Household Participation in Community Forest Management? Evidence from the Middle Hills of Nepal

Rural out-migration was a rare socio-economic phenomenon when community forestry began in the 1980s in Nepal. Now, out-migration significantly influences nearly every aspect of rural livelihoods in the country. However, it is unclear how out-migration affects community forestry governance, which is essential for sustainable rural development. Therefore, this paper addresses the following research question: Does rural out-migration affect forest users’ participation in community forestry decision-making and management practices? This paper draws on data collected from an extensive survey of 415 households from 15 community forest user groups in 2 Mid-Hill districts of Nepal. The research used ordered-logit regression to model the impacts of out-migration on participation in forest management and decision-making, while controlling for a number of other socio-economic factors. The model results show that total household size and number of internal migrants, together with multiple resource characteristics and institutional attributes, were major factors affecting participation in decision-making and forest management. However, the number of international migrants did not have a significant role in determining the levels of the participation. This study provides valuable insights for future community forestry policymaking that aims to address the effects of out-migration on community forest management in Nepal.

Trees form factor in Tropical Dry Forest in north of Tolima – Colombia

The high biodiversity can make it difficult to select dendrometric models for each ecosystem. In order to contribute to these models, a relict tropical dry forest of approximately 100 hectares was selected in the valley of the Magdalena River, located in the central region of Colombia. This forest relict is in the municipality of Venadillo, department of Tolima. A sample of 36 trees of different species with a normal diameter greater than 10 centimeters were randomly selected. All trees were measured using a non-destructive method. Diameters with bark were measured on the trunks of the standing trees at different heights from the base of the tree to its commercial height using Field Map equipment and technology. With the information collected, a tree form factor was determined to be used as a forestry decision tool or as an element of comparison with similar ecosystems. The form factor for the calculated commercial volume was 0.8.

Shifting tree species composition affects biodiversity of multiple taxa in Central European forests

Central Europe's temperate forests are heavily shaped by centuries of human activity. Their natural vegetation, mainly consisting of beech-dominated (Fagus sylvatica) deciduous forests, has been widely replaced by more profitable species grown outside of their natural ranges. This has strongly influenced forest-dwelling communities. Necessary adaptations to changing climatic conditions and the increasing demand for forest ecosystem multifunctionality are reversing these shifts in tree species composition. Integrative approaches that seek to balance production and conservation goals promote mixed forests of beech with spruce (Picea abies), pine (Pinus sylvestris), or oak (Quercus spp.). These mixed forests more closely resemble the natural vegetation and have reduced vulnerability to disturbances compared to coniferous monocultures, but higher commercial value compared to pure beech forests. However, our understanding of how different levels of admixture of commercially relevant tree species to beech forests affect multi-trophic diversity and community composition remains limited.We investigated herbaceous plants, fungi, oribatid mites, springtails, true bugs, beetles, birds and bats in 41 mature forest stands differing in tree species composition. We assessed the effects of admixtures on abundances and alpha and gamma diversity, i.e. the total number of species per forest type, and a measure of multidiversity by comparing reference beech stands with stands containing varying proportions of admixed species. At the plot level, the proportion of admixtures was especially important regarding oak and pine. Increasing shares of oak positively affected birds, true bugs and herbivorous beetles. Increasing shares of pine benefitted herbivorous true bugs and understory plants but negatively affected other true bugs, bats, and litter-decomposing fungi. Spruce admixture resulted in higher saproxylic beetle and bird diversity. At the landscape level, admixture significantly increased gamma diversity in plants, mycorrhizal and litter decomposing fungi and herbivorous and saproxylic beetles. Only springtail gamma diversity decreased in the presence of admixture. Admixture also significantly altered community composition for six out of 13 taxa. Indicator species were found for all forest types, and seven species groups included species significantly associated with pure beech stands.Our results indicate that forestry decisions determine forest biodiversity across trophic levels via tree species composition, combining habitat heterogeneity effects and tree species-specific associations. Even low shares of admixed species affect local abundances and diversity. By displacing some species while benefitting others, admixing also alters community composition. This study provides a basis for estimating how altering tree species composition in Central European forests changes the diversity and composition of forest communities.

Assessing economic value of community forestry for sustainable rural development

Community forestry contributes to the rural economy in a variety of ways: directly as a consumer of land and services to convert biological and other inputs into a variety of outputs; indirectly through its linkages with upstream producers and downstream processing sectors; and indirectly through the re-spending in rural areas of portions of income generated from forestry and related industries. through the provision of non-market benefits; and a pleasant living atmosphere for many people. This study aims to estimate the economic value of community forests in Pajangan, Bantul district. The benefits of community forests can be direct use values and indirect use values. The benefits of Pajangan community forests are estimated using the Total Economic Value (TEV) method. The result shows that TEV of IDR 70,298,307,526 per year, consisting of direct use value of IDR 9,344,000,000 per year, the indirect use value of IDR 35,274,178,836 per year. It is critical to provide economic incentives for communities to participate in sustainable rural development such as ensuring that the full economic value of forests is recognized and reflected in both economic and forestry decision-making, with a focus on economic costs and benefits that accrue at the community level.

A comparison of hydrological characteristics between a cypress and mixed-broadleaf forest: Implication on water resource and floods

Broadleaf or conifer forest? – an ongoing dilemma as regards water resource and flood prevention. Although conifer trees use less water and are hydrophobic, existing studies at the watershed scale have shown varying results due to variability in meteorological conditions and forestry practices. In this study, two adjacent headwater catchments [2.42 ha cypress (CF); 2.08 ha mixed-broadleaf (MB)] were gauged. The study area in central Japan has a warm temperate climate with high annual rainfall and typhoons. Daily discharge (n = 1398) and storm event hydrographs (n = 242) were compared between CF and MB and discussed in light of water availability and storm event runoff characteristics. Overall water yield in CF was 6.4% lower than in MB but this was caused by lower streamflow on high discharge periods (30% frequency); thus, may not adversely affect municipal/agricultural water supply. For 70% of the time including during dry periods, water yield was higher in CF. This agrees with the principle that conifers use less water than broadleaf trees – even at the forested catchment scale. Discharge in the CF stream is also less variable than that in the MB stream. During storm events, the CF stream has higher flashiness (shorter response time to rainfall and higher peak discharge) than the MB stream; but in larger storms (≳70 mm), the MB stream may have higher peak discharge. Our data demonstrates that a well-managed plantation forest does not necessarily have inferior hydrological function compared to a natural broadleaf forest, which differs from the stereotype. Frequencies of high and low flows as well as runoff characteristics were also presented in hopes of promoting better forestry decisions when considering water demand and existing stormwater drainage infrastructures.

Beyond 'trees are good': Disservices, management costs, and tradeoffs in urban forestry.

The provision of ecosystem services is a prominent rationale for urban greening, and there is a prevailing mantra that 'trees are good'. However, understanding how urban trees contribute to sustainability must also consider disservices. In this perspective article, we discuss recent research on ecosystem disservices of urban trees, including infrastructure conflicts, health and safety impacts, aesthetic issues, and environmentally detrimental consequences, as well as management costs related to ecological disturbances and risk management. We also discuss tradeoffs regarding species selection and local conservation concerns, as well as the central role of human perception in the interpretation of ecosystem services and disservices, particularly the uncritical assertion that 'everybody loves trees'. Urban forestry decision-making that fails to account for disservices can have unintended negative consequences for communities. Further research is needed regarding life cycle assessments, stakeholder decision-making, return-on-investment, and framings of services and disservices in urban forestry.

Modeling of ash (Fraxinus excelsior) bark thickness in urban forests using artificial neural network (ANN) and regression models

Bark is a protective layer in trees, as well as living texture, so estimating bark thickness is very important in future decisions in urban forestry. The purpose of this article is to compare artificial neural network (ANN) and regression modeling to prepare the most accurate model for predicting the thickness of ash bark and effective variables in the urban forestry. A total of 10 variables including age of tree, collar diameter, diameter at the breast height, slope of habitat, aspect of habitat, altitude, wind direction, direction of wind protection, direction of light on crown and direction of light on the 600 ash trees were measured. Based on SPSS analysis, the age of tree variable had the greatest effect on bark thickness. Among the models of linear regression, S model with a R2 of 0.616 was selected as the best model. Artificial neural network model was designed simulated in the Neuro-Solutions ver.5 software. Multi- layer perceptron network with two hidden layers and eight neurons makes the best function for optimizing topology with highest coefficient of determination ~ 0.830. Based on the results of sensitivity analysis, tree age was the most important factor influencing bark thickness. Finally, the model of ANN with higher coefficient of determination and less error was determined as the best model in predicting ash bark thickness in urban forests.

Improving Economic Management Decisions in Forestry with the SorSim Assortment Model

The sustainable supply of timber is one of the most important forest ecosystem services and a decisive factor determining the long-term profitability of forest enterprises. If timber production is to be economically viable, there must always be a way to analyse forest stands and trees felled for exploitation with regard to the wood assortments they contain. Only then can the expected timber yields, achieved by various silvicultural strategies or actions and different sorting options, be quantified with sufficient accuracy. The SorSim assortment simulator was developed for forest practitioners and forest scientists in Switzerland to realistically simulate the sorting of individual trees and entire forest stands based on defined specifications. SorSim has a simple user interface and comes in a number of different language versions (G, E, F). The software is implemented in Java, making it platform-independent. It can be downloaded for free at (https://www.wsl.ch/en/projects/sortimentsimulator-sorsim.html). This article provides an overview of how the simulator works and demonstrates its potential applications based on a practical and a scientific example. A particular practical advantage is that the composition of the assortments of the planned harvests can be estimated according to quantity and value. When used in strategic planning and especially in research, SorSim provides a basis for analysing either long-term developments in yields from forest stands or silvicultural treatment methods. Based on an even-aged and a selection forest stand, the scientific example shows how strongly the assessment of the advantageousness of two different silvicultural strategies depends on the time when the calculation was made (using historical and current assortment revenues and timber harvesting costs). In particular, the combination of SorSim with timber harvest productivity models enables differentiated forest economic insights. Various approaches for value-based optimisation in the sorting of individual trees and for the optimal allocation of harvesting activities to defined customer demands are currently being examined as further SorSim developments.

Development of a dynamic growth model for sweet chestnut coppice: A case study in Northwest Spain

Sweet chestnut coppice (Castanea sativa Mill.) is a species of great importance in the northwest of Spain, due to its potential for producing valuable timber in relatively short rotations. However, abandonment has resulted in unstable and degraded stands. Thus, there is a need to improve forestry decision making tools. The objective of this study is the development of a dynamic stand growth model for the sweet chestnut comprised of three transition functions (dominant height, basal area and number of stems per hectare). They are used to estimate rates of change in the stand between an initial point in time and a point in the future. The data comes from two inventories of an unmanaged network of plots which incorporate all the variability in conditions in the region for the study species (climate, soil, stocking, site quality etc.). ADA and GADA approaches were used to develop the three transition functions. The model achieved high accuracy (explaining >90% of variability). The model incorporates an initialization function (explaining 60% of variability) for predicting initial stand basal area in stands without diameter inventories, which can be used to establish the starting point for the simulation, and, in addition, the biomass expansion factor (BEF) for this species (expressed as a constant value of 0.60) and a new single aboveground biomass equation (explaining almost 80% of variability) were calculated. A case study shows how to apply these decision making tools for the sustainable management of sweet chestnut coppice.

Simulating the effects of wind and snow damage on the optimal management of Norwegian spruce forests

AbstractOverlooking the risk of wind and snow damage in forest planning may lead to suboptimal management prescriptions. In this study, we analysed the optimal management of an even-aged, spruce-dominated stand in Norway under the risk of snow and wind damage. The management aim was to maximize discounted net revenues of timber production. We used a simulation-optimization system based on models for stand dynamics and damage, using either deterministic or stochastic approach to consider risk. The different approaches to simulating damage resulted in 41 optimization cases. The results show that considering risk leads to earlier cuttings, lower growing stock densities towards the end of the rotations and changes in the number and intensity of thinnings. The inclusion of stochastic damage provided a valid approach for considering the uncertainty associated with the risk of damage. Ignoring the effect of wind and snow damage in the calculations resulted in up to 25 per cent overestimation of the revenues. The results from this study will help to integrate the risk of natural disturbances into forestry decision-making, and provide a better understanding of the implications that snow and wind damage have on optimal forest management.

Decline and dieback of cork oak (Quercus suber L.) forests in the Mediterranean basin: a case study of Kroumirie, Northwest Tunisia

Assessing the vulnerability of forest ecosystems in the climate change context is a challenging task as the mechanisms that determine this vulnerability cannot be directly observed. Based on the ecological interrelationships between forests and climate, the present review focused on providing current information about vulnerability assessments of cork oak (Quercus suber L.) forests in the Mediterranean basin, especially, in the Kroumirie region (northwest Tunisia), currently under historic extreme drought conditions. From comparing recent findings in this region, we synthesized data on cork oak decline and mortality collected during the historic drought years 1988–1995 period. Climate change impacts cork forest decline, with special interest shown in elevated temperatures and drought; cork oak forest regeneration, and the adaptation of the Kroumirie forest to climate change, are reviewed herein. The studied region has been influenced largely by frequent prolonged drought periods, especially from 1988 to 1995. Droughts were found to consistently have a more detrimental impact on the growth and mortality rates of cork oak populations. Cork oak mortality was recorded for up to 63,622 trees. In the future, more research studies and observational data will be needed, which could represent an important key to understand ecosystem processes, and to facilitate the development of better models that project climate change impacts and vulnerability. The study is useful for researchers and forestry decision makers to develop the appropriate strategies to restore and protect ecosystems, and to help anticipate potential future droughts and climate change.

Leaf gas exchange variation under summer drought in Tunisian cork oak from geographically central and marginal populations

A global understanding of the ability of cork oak populations to cope with and adapt to summer drought stress is crucial for assessments of the future of cork oak forests as a major ecosystem in the Mediterranean. We examined the effect of summer drought on photosynthetic capacity in three Quercus suber (L.) populations that are native to different ecological conditions. Djebal Chahid (JCH) and Kef Errand (KER) originated from the driest sites and Feija (FEJ) from the wettest site. FEJ is the central area; however, JCH and KER are from geographically marginal sites of the species distribution range in Tunisia. The photosynthetic performance was monitored by measuring the leaf gas exchange and chlorophyll fluorescence parameters. The FEJ population leaves showed the lowest light-saturated rates of assimilation (Amax). However, the photosynthetic response curves as a function of photon flux density for leaves of different populations showed that the light compensation point was higher in FEJ compared to KER and JCH. The apparent quantum yield (Φ) was not significantly different between all the studied populations. The specific leaf area and chlorophyll content were similar in leaves of the studied populations, whilst assimilation seemed to be limited by stomatal factors. Our finding confirms previous studies showing a large population-level differentiation in cork oak with population from dry sites exhibiting a higher tolerance. This is useful to the forestry decision-makers to develop appropriate strategies to restore and protect forest ecosystems.