Effects of tree mixture on forest productivity: tree species addition versus substitution

2021 ◽  
Author(s):  
Maude Toïgo ◽  
Bastien Castagneyrol ◽  
Hervé Jactel ◽  
Xavier Morin ◽  
Celine Meredieu
Keyword(s):  
Tree Species ◽  
Forest Productivity

scholarly journals Do rising temperatures always increase forest productivity? Interacting effects of temperature, precipitation, cloudiness and soil texture on tree species growth and competition

2017 ◽  
Vol 97 ◽  
pp. 171-183 ◽  
Author(s):  
Eric J. Gustafson ◽  
Brian R. Miranda ◽  
Arjan M.G. De Bruijn ◽  
Brian R. Sturtevant ◽  
Mark E. Kubiske
Keyword(s):  
Soil Texture ◽  
Tree Species ◽  
Forest Productivity ◽  
Effects Of Temperature

scholarly journals Long-term response of forest productivity to climate change is mostly driven by change in tree species composition

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Xavier Morin ◽  
Lorenz Fahse ◽  
Hervé Jactel ◽  
Michael Scherer-Lorenzen ◽  
Raúl García-Valdés ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Composition ◽  
Tree Species ◽  
Forest Productivity ◽  
Tree Species Composition ◽  
Term Response

scholarly journals The Influence of Tree Structural and Species Diversity on Temperate Forest Productivity and Stability in Korea

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1113 ◽  
Author(s):  
Juhan Park ◽  
Hyun Seok Kim ◽  
Hyun Kook Jo ◽  
II Bin Jung
Keyword(s):  
Species Diversity ◽  
Tree Species ◽  
Temperate Forest ◽  
Temperate Forests ◽  
Basal Area ◽  
Stand Density ◽  
Structural Diversity ◽  
Forest Productivity ◽  
Tree Species Diversity ◽  
Positive Effect

Research Highlights: Using a long-term dataset on temperate forests in South Korea, we established the interrelationships between tree species and structural diversity and forest productivity and stability, and identified a strong, positive effect of structural diversity, rather than tree species diversity, on productivity and stability. Background and Objectives: Globally, species diversity is positively related with forest productivity. However, temperate forests often show a negative or neutral relationship. In those forests, structural diversity, instead of tree species diversity, could control the forest function. Materials and Methods: This study tested the effects of tree species and structural diversity on temperate forest productivity. The basal area increment and relative changes in stand density were used as proxies for forest productivity and stability, respectively. Results: Here we show that structural diversity, but not species diversity, had a significant, positive effect on productivity, whereas species diversity had a negative effect, despite a positive effect on diversity. Structural diversity also promoted fewer changes in stand density between two periods, whereas species diversity showed no such relation. Structurally diverse forests might use resources efficiently through increased canopy complexity due to canopy plasticity. Conclusions: These results indicate reported species diversity effects could be related to structural diversity. They also highlight the importance of managing structurally diverse forests to improve productivity and stability in stand density, which may promote sustainability of forests.

scholarly journals Hydraulic diversity stabilizes productivity in a large-scale subtropical tree biodiversity experiment

2021 ◽  
Author(s):  
Florian Schnabel ◽  
Xiaojuan Liu ◽  
Matthias Kunz ◽  
Kathryn E. Barry ◽  
Franca J. Bongers ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Richness ◽  
Tree Species ◽  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Forest Productivity ◽  
Climate Mitigation ◽  
Climate Change Effects ◽  
Tree Species Richness

AbstractExtreme climatic events threaten forests and their climate mitigation potential globally. Understanding the drivers promoting ecosystems stability is therefore considered crucial to mitigate adverse climate change effects on forests. Here, we use structural equation models to explain how tree species richness, asynchronous species dynamics and diversity in hydraulic traits affect the stability of forest productivity along an experimentally manipulated biodiversity gradient ranging from 1 to 24 tree species. Tree species richness improved stability by increasing species asynchrony. That is at higher species richness, inter-annual variation in productivity among tree species buffered the community against stress-related productivity declines. This effect was mediated by the diversity of species’ hydraulic traits in relation to drought tolerance and stomatal control, but not the community-weighted means of these traits. Our results demonstrate important mechanisms by which tree species richness stabilizes forest productivity, thus emphasizing the importance of hydraulically diverse, mixed-species forests to adapt to climate change.

scholarly journals Neither global nor consistent: a technical comment on the tree diversity-productivity analysis of Liang et al. (2016)

2019 ◽  
Author(s):  
Carsten F. Dormann ◽  
Helge Schneider ◽  
Jonas Gorges
Keyword(s):  
Species Richness ◽  
Tree Species ◽  
Forest Productivity ◽  
Local Scale ◽  
Tree Diversity ◽  
Productivity Analysis ◽  
Combine Data ◽  
Relative Measure ◽  
Tree Species Richness ◽  
Global Consistency

AbstractThe publication of Liang et al. (2016, Science) seems to demonstrate very clearly that increasing tree species richness substantially increases forest productivity. To combine data from very different ecoregions, the authors constructed a relative measure of tree species richness. This relative richness however confounds plot-level tree species richness and the polar-tropical gradient of tree species richness. We re-analysed their orginal data, computing a regional measure of tree species richness and addressing several other issues in their analysis. We find that there is virtually no effect of relative tree species richness on productivity when computing species richness at the local scale. Also, different ecoregions have very different relationships between tree species richness and productivity. Thus, neither the “global” consistency nor the actual effect can be confirmed.

A disturbance-induced increase in tree species diversity facilitates forest productivity

2015 ◽  
Vol 31 (5) ◽  
pp. 989-1004 ◽  
Author(s):  
Mariana Silva Pedro ◽  
Werner Rammer ◽  
Rupert Seidl
Keyword(s):  
Species Diversity ◽  
Tree Species ◽  
Forest Productivity ◽  
Tree Species Diversity

Comment on “Impacts of species richness on productivity in a large-scale subtropical forest experiment”

Science ◽  
2019 ◽  
Vol 363 (6423) ◽  
pp. eaav9117
Author(s):  
Hua Yang ◽  
Zhongling Guo ◽  
Xiuli Chu ◽  
Rongzhou Man ◽  
Jiaxin Chen ◽  
...  
Keyword(s):  
Species Richness ◽  
Tree Species ◽  
Large Scale ◽  
Forest Productivity ◽  
Subtropical Forest ◽  
Species Loss ◽  
Subtropical China

Huang et al. (Reports, 5 October 2018, p. 80) report significant increases in forest productivity from monocultures to multispecies mixtures in subtropical China. However, their estimated productivity decrease due to a 10% tree species loss seems high. We propose that including species richness distribution of the study forests would provide more meaningful estimates of forest-scale responses.

Climate- and soil-based models of site productivity in eastern US tree species

2015 ◽  
Vol 45 (3) ◽  
pp. 325-342 ◽  
Author(s):  
Huiquan Jiang ◽  
Philip J. Radtke ◽  
Aaron R. Weiskittel ◽  
John W. Coulston ◽  
Patrick J. Guertin
Keyword(s):  
Climate Change ◽  
Soil Properties ◽  
Tree Species ◽  
Prediction Models ◽  
Forest Productivity ◽  
Site Index ◽  
Soil Survey ◽  
Specific Site ◽  
Ensemble Prediction ◽  
Species Specific

As concerns rise over potential effects of greenhouse gas related climate change on terrestrial ecosystems, forest managers require growth and yield modeling capabilities responsive to changing climate conditions. Our goal was to develop prediction models of site index for eastern US forest tree species with climate and soil properties as predictors for use in predicting potential responses of forest productivity to climate change. Species-specific site index data from the USDA Forest Service Forest Inventory and Analysis (FIA) program were linked to contemporary climate data and soil properties mapped in the USDA Soil Survey Geographic (SSURGO) database. Random forest regression tree based ensemble prediction models of site index were constructed based on 37 climate-related and 15 soil attributes. In addition to a species-specific site index, aggregate models were developed for species grouped into two broad categories: conifer (softwood) and hardwood (broadleaved) species groups. Species-specific models based on climate and soil predictors explained the most variation in site index of any models tested (R2= 62.5%, RMSE = 3.2 m). Comparable results were found when grouping species into conifer and hardwood groups (R2= 63.9%, RMSE = 4.6 m for conifers; R2= 35.9%, RMSE = 4.2 m for hardwoods). Model predictions based on multiple global circulation models (GCMs) and Intergovernmental Panel on Climate Change (IPCC) development scenarios were tested for statistical significance using bootstrap resampling methods. Results showed significant increases over the 21st century in mean site index for conifers between +0.5 and +2.4 m. Over the same time period, mean hardwood site index showed decreases of as much as −1.7 m for the scenarios tested. The results demonstrate the utility of using climate and soils data in predicting site index across a large geographic region, and the potential of climate change to alter forest productivity in the eastern US. Additional investigation is needed to interpret spatial patterns and ecological relationships related to predictions from this type of model.

Assessing responses to climate stressors in two contrasting pine species

2020 ◽  
Author(s):  
Daniel Nadal-Sala ◽  
Benjamin Birami ◽  
Romy Rehschuh ◽  
Marielle Gattmann ◽  
Ruediger Grote ◽  
...  
Keyword(s):  
Scots Pine ◽  
Tree Species ◽  
Forest Productivity ◽  
Environmental Stressors ◽  
Aleppo Pine ◽  
Controlled Experiments ◽  
Climate Conditions ◽  
Ecophysiological Traits ◽  
The Mediterranean ◽  
Pine Species

<p>Climate conditions in which tree species are able to grow are determined by their ecophysiological traits. The genus Pinus spp. is widespread across Eurasia, so that the different Pinus species have evolved to live within diverse climate envelops, from the boreal Scots pine (Pinus sylvestris L.) to the Mediterranean Aleppo pine (Pinus halepensis Mill.). Therefore, the different pine species are expected to present contrasting responses to environmental stressors, depending on the ones that populations had faced in the past.</p><p>Here we analyze the impact of climate on stand carbon fluxes in two contrasting stands -i.e. a boreal Finnish Scots pine stand and a Mediterranean Israeli Aleppo pine stand. We use a machine learning approach -i.e. Random Forest algorithm- to evaluate seasonal changes in the most limiting environmental driver (MLED) for forest productivity. Then, we use data from controlled experiments with Aleppo and Scots pine saplings, in which we evaluated their response to drought and heat stresses, in order to assess if differences in their ecophysiological traits may explain their ability to grow in such contrasting climate conditions.</p><p>Our results suggest that the MLED of forest productivity during all year in the boreal stand are low temperatures. Conversely but not surprisingly, the MLED in the Mediterranean stand is soil water availability, especially during summer. Therefore, we expect P. halepensis to be better adapted to heat and drought stresses, whereas we expect P. sylvestris to present higher photosynthetic rates at lower temperatures. Controlled experiments confirm these expectations, with a remarkable isohydric behavior of P. halepensis during drought, and different species responses of photosynthesis thermal optimum to heat and drought stress. Our results highlight the need to understand how traits determine tree species’ responses to different environmental stressors, in order to anticipate their performance in a warmer world.</p>

scholarly journals A Combination of Biotic and Abiotic Factors and Diversity Determine Productivity in Natural Deciduous Forests

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1450
Author(s):  
Mahmoud Bayat ◽  
Pete Bettinger ◽  
Sahar Heidari ◽  
Seyedeh Kosar Hamidi ◽  
Abolfazl Jaafari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Species Richness ◽  
Species Diversity ◽  
Tree Species ◽  
Abiotic Factors ◽  
Basal Area ◽  
Forest Productivity ◽  
Tree Species Diversity ◽  
Artificial Neural

The relative importance of different biotic and abiotic variables for estimating forest productivity remains unclear for many forest ecosystems around the world, and it is hypothesized that forest productivity could also be estimated by local biodiversity factors. Using a large dataset from 258 forest monitoring permanent sample plots distributed across uneven-aged and mixed forests in northern Iran, we tested the relationship between tree species diversity and forest productivity and examined whether several factors (solar radiation, topographic wetness index, wind velocity, seasonal air temperature, basal area, tree density, basal area in largest trees) had an effect on productivity. In our study, productivity was defined as the mean annual increment of the stem volume of a forest stand in m3 ha−1 year−1. Plot estimates of tree volume growth were based on averaged plot measurements of volume increment over a 9-year growing period. We investigated relationships between productivity and tree species diversity using parametric models and two artificial neural network models, namely the multilayer perceptron (MLP) and radial basis function networks. The artificial neural network (ANN) of the MLP type had good ability in prediction and estimation of productivity in our forests. With respect to species richness, Model 4, which had 10 inputs, 6 hidden layers and 1 output, had the highest R2 (0.94) and the lowest RMSE (0.75) and was selected as the best species richness predictor model. With respect to forest productivity, MLP Model 2 with 10 inputs, 12 hidden layers and 1 output had R2 and RMSE of 0.34 and 0.42, respectively, representing the best model. Both of these used a logistic function. According to a sensitivity analysis, diversity had significant and positive effects on productivity in species-rich broadleaved forests (approximately 31%), and the effects of biotic and abiotic factors were also important (29% and 40%, respectively). The artificial neural network based on the MLP was found to be superior for modeling productivity–diversity relationships.

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