A climate-sensitive transition matrix growth model for uneven-aged mixed-species oak forests in North China

2020 ◽  
Author(s):  
Xue Du ◽  
Xinyun Chen ◽  
Weisheng Zeng ◽  
Jinghui Meng
Keyword(s):  
Climate Change ◽  
Growth Model ◽  
Transition Matrix ◽  
Forest Growth ◽  
Oak Forests ◽  
Mixed Species ◽  
Short Term ◽  
Term Prediction ◽  
Long Term Prediction

Abstract Oak-dominated forests, economically and ecologically valuable ecosystems, are widely distributed in China. These oak-dominated forests are now generally degraded coppice forests, and are of relatively low quality. Climate change has been shown to affect forest growth, tree mortality, and recruitment, but available forest growth models are lacking to study climate effects. In this study, a climate-sensitive, transition-matrix growth model (CM) was developed for uneven-aged, mixed-species oak forests using data collected from 253 sample plots from the 8th (2010) and 9th (2015) Chinese National Forest Inventory in Shanxi Province, China. To investigate robustness of the model, we also produced a variable transition model that did not consider climate change (NCM), and fixed parameter transition matrix model (FM), using the same data. Short-term and long-term predictive performance of CM, NCM, and FM were compared. Results indicated that for short-term prediction (5 years), there was almost no significant difference among the three predictive models, though CM exhibited slightly better performance. In contrast, for long-term prediction (100 years), CM, under the three representative concentration pathways (RCPs), i.e. RCP2.6, RCP4.5 and RCP8.5, indicated rather different dynamics that were more reliable because climate factors were considered which could significantly influence forest dynamics, especially in long-term prediction intervals. The CM model provides a framework for the management of mixed-species oak forests in the context of climate change.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

scholarly journals Novel Robust Time Series Analysis for Long-term and Short-term Prediction

2021 ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

Abstract Nonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with traditional least squares and least absolute deviations methods using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner--recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas the other methods fail to estimate autocorrelation accurately.

scholarly journals Beyond forest succession: a gap model to study ecosystem functioning and tree community composition under climate change

2020 ◽  
Author(s):  
Xavier Morin ◽  
François de Coligny ◽  
Nicolas Martin-StPaul ◽  
Harald Bugmann ◽  
Maxime Cailleret ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Composition ◽  
Community Composition ◽  
Ecosystem Functioning ◽  
Forest Growth ◽  
Gap Model ◽  
Forest Stands ◽  
Short Term ◽  
Gap Models

ABSTRACTClimate change impacts forest functioning and dynamics, and large uncertainties remain regarding the interactions between species composition, demographic processes, and environmental drivers. There are few robust tools available to link these processes, which precludes accurate projections and recommendations for long-term forest management. Forest gap-models present a balance between complexity and generality and are widely used in predictive forest ecology. However, their relevance to tackle questions about the links between species composition, climate and forest functioning is unclear. In this regard, demonstrating the ability of gap-models to predict the growth of forest stands at the annual time scale – representing a sensitive and integrated signal of tree functioning and mortality risk - appears as a fundamental step.In this study, we aimed at assessing the ability of a gap-model to accurately predict forest growth in the short-term and potential community composition in the long-term, across a wide range of species and environmental conditions. To do so, we present the gap-model ForCEEPS, calibrated using an original parameterization procedure for the main tree species in France. ForCEEPS was shown to satisfactorily predict forest annual growth (averaged over a few years) at the plot level from mountain to Mediterranean climates, regardless the species. Such an accuracy was not gained at the cost of losing precision for long-term predictions, as the model showed a strong ability to predict potential community composition along a gradient of sites with contrasted conditions. The mechanistic relevance of ForCEEPS parameterization was explored by showing the congruence between the values of key model parameter and species functional traits. We further showed that accounting for the spatial configuration of crowns within forest stands, the effects of climatic constraints and the variability of shade tolerances in the species community are all crucial to better predict short-term productivity with gap-models.The dual ability of predicting short-term functioning and long-term community composition, as well as the balance between generality and realism (i.e., predicting accuracy) of the new generation of gap-models may open great perspectives for the exploration of the biodiversity-ecosystem functioning relationships, species coexistence mechanisms, and the impacts of climate change on forest ecosystems.

scholarly journals Empirical regional models for the short-term forecast of <i>M3000F2</i> during not quiet geomagnetic conditions over Europe

2013 ◽  
Vol 31 (10) ◽  
pp. 1653-1671 ◽  
Author(s):  
M. Pietrella
Keyword(s):  
Correction Factor ◽  
Regional Model ◽  
Local Models ◽  
Short Term ◽  
European Area ◽  
Term Prediction ◽  
Starting Point ◽  
Short Term Forecasting ◽  
Long Term Prediction

Abstract. Twelve empirical local models have been developed for the long-term prediction of the ionospheric characteristic M3000F2, and then used as starting point for the development of a short-term forecasting empirical regional model of M3000F2 under not quiet geomagnetic conditions. Under the assumption that the monthly median measurements of M3000F2 are linearly correlated to the solar activity, a set of regression coefficients were calculated over 12 months and 24 h for each of 12 ionospheric observatories located in the European area, and then used for the long-term prediction of M3000F2 at each station under consideration. Based on the 12 long-term prediction empirical local models of M3000F2, an empirical regional model for the prediction of the monthly median field of M3000F2 over Europe (indicated as RM_M3000F2) was developed. Thanks to the IFELM_foF2 models, which are able to provide short-term forecasts of the critical frequency of the F2 layer (foF2STF) up to three hours in advance, it was possible to considerer the Brudley–Dudeney algorithm as a function of foF2STF to correct RM_M3000F2 and thus obtain an empirical regional model for the short-term forecasting of M3000F2 (indicated as RM_M3000F2_BD) up to three hours in advance under not quiet geomagnetic conditions. From the long-term predictions of M3000F2 provided by the IRI model, an empirical regional model for the forecast of the monthly median field of M3000F2 over Europe (indicated as IRI_RM_M3000F2) was derived. IRI_RM_M3000F2 predictions were modified with the Bradley–Dudeney correction factor, and another empirical regional model for the short-term forecasting of M3000F2 (indicated as IRI_RM_M3000F2_BD) up to three hours ahead under not quiet geomagnetic conditions was obtained. The main results achieved comparing the performance of RM_M3000F2, RM_M3000F2_BD, IRI_RM_M3000F2, and IRI_RM_M3000F2_BD are (1) in the case of moderate geomagnetic activity, the Bradley–Dudeney correction factor does not improve significantly the predictions; (2) under disturbed geomagnetic conditions, the Bradley–Dudeney formula improves the predictions of RM_M3000F2 in the entire European area; (3) in the case of very disturbed geomagnetic conditions, the Bradley–Dudeney algorithm is very effective in improving the performance of IRI_RM_M3000F2; (4) under moderate geomagnetic conditions, the long-term prediction maps of M3000F2 generated by RM_M3000F2 can be considered as short-term forecasting maps providing very satisfactory results because quiet geomagnetic conditions are not so diverse from moderate geomagnetic conditions; (5) the forecasting maps originated by RM_M3000F2, RM_M3000F2_BD, and IRI_RM_M3000F2_BD show some regions where the forecasts are not satisfactory, but also wide sectors where the M3000F2 forecasts quite faithfully match the M3000F2 observations, and therefore RM_M3000F2, RM_M3000F2_BD, and IRI_RM_M3000F2_BD could be exploited to produce short-term forecasting maps of M3000F2 over Europe up to 3 h in advance.

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