呼伦贝尔草原区CO2源、汇及时空分布模拟研究

近年来,随着全球气候变化和人为影响加剧,半干旱草地生态系统的碳循环受到剧烈影响。半干旱草原区域CO_2模拟研究主要集中于已有观测资料的地区,然而,观测资料缺乏的草原区CO_2通量模拟却鲜少有人研究。因此选择缺通量资料的呼伦贝尔草原地区为主要研究对象,并将VPRM模型应用于缺资料地区,模拟了该区域内2016年的NEE时空分布。结果表明:(1)在特旱年的气候条件下2016年全年都表现为微弱的碳源(全年NEE值为47.27 gC/m~2),且其变化趋势与降水和气温在年内变化趋势相近。(2)空间上,根据趋势来看NEE在空间分布由草原区向草甸区、森林区逐渐降低。基于植被分布情况,不同植被类型的区域碳排放顺序为:克氏针茅草原和大针茅草原羊草草原杂草草甸草原(以线叶菊等为主)。(3)干旱胁迫是该地区表现为碳源的主要原因之一,而且降水与NEE表现出极显著的二次函数关系(R~2=0.938,P0.001),说明了干旱气候条件下,随着月降水量的增加,草原生态系统出现碳源向碳汇转移的趋势。(4)地上生物量(AGB)与GPP和Reco表现出了极显著的正相关关系(R~2分别为0.89和0.9,P0.01),与NEE表现出了极显著的负相关关系(R~2=0.68,P0.01),说明了草原的地上生物量增加能有效地降低二氧化碳排放。     

Nitrogen limitation on land: how can it occur in Earth system models?

The representation of the nitrogen (N) cycle in Earth system models (ESMs) is strongly motivated by the constraint N poses on the sequestration of anthropogenic carbon (C). Models typically implement a stoichiometric relationship between C and N in which external supply and assimilation by organisms are adjusted to maintain their internal stoichiometry. N limitation of primary productivity thus occurs if the N supply from uptake and fixation cannot keep up with the construction of tissues allowed by C assimilation. This basic approach, however, presents considerable challenges in how to faithfully represent N limitation. Here, we review how N limitation is currently implemented and evaluated in ESMs and highlight challenges and opportunities in their future development. At or near steady state, N limitation is governed by the magnitude of losses from the plant‐unavailable pool vs. N fixation and there are considerable differences in how models treat both processes. In nonsteady‐state systems, the accumulation of N in pools with slow turnover rates reduces N available for plant uptake and can be challenging to represent when initializing ESM simulations. Transactional N limitation occurs when N is incorporated into various vegetation and soil pools and becomes available to plants only after it is mineralized, the dynamics of which depends on how ESMs represent decomposition processes in soils. Other challenges for ESMs emerge when considering seasonal to interannual climatic oscillations as they create asynchronies between C and N demand, leading to transient alternations between N surplus and deficit. Proper evaluation of N dynamics in ESMs requires conceptual understanding of the main levers that trigger N limitation, and we highlight key measurements and observations that can help constrain these levers. Two of the biggest challenges are the mechanistic representation of plant controls on N availability and turnover, including N fixation and organic matter decomposition processes.     

Quasi Likelihood/Moment Method for Generalized and Restricted Generalized Poisson Regression Models and Its Application

This paper reviews the generalized Poisson regression model, the restricted generalized Poisson regression model and the mixed Poisson regression (negative binomial regression and Poisson inverse Gaussian regression) models which can be used for regression analysis of counts. The aim of this study is to demonstrate the quasi likelihood/moment method, which is used for estimation of the parameters of mixed Poisson regression models, also applicable to obtain the estimates of the parameters of the generalized Poisson regression and the restricted generalized Poisson regression models. Besides, at the end of this study an application related to this method for zoological data is given.     

Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction

The task of modeling the distribution of a large number of tree species under future climate scenarios presents unique challenges. First, the model must be robust enough to handle climate data outside the current range without producing unacceptable instability in the output. In addition, the technique should have automatic search mechanisms built in to select the most appropriate values for input model parameters for each species so that minimal effort is required when these parameters are fine-tuned for individual tree species. We evaluated four statistical models—Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS)—for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model. To test, we applied these techniques to four tree species common in the eastern United States: loblolly pine (Pinus taeda), sugar maple (Acer saccharum), American beech (Fagus grandifolia), and white oak (Quercus alba). When the four techniques were assessed with Kappa and fuzzy Kappa statistics, RF and BT were superior in reproducing current importance value (a measure of basal area in addition to abundance) distributions for the four tree species, as derived from approximately 100,000 USDA Forest Service’s Forest Inventory and Analysis plots. Future estimates of suitable habitat after climate change were visually more reasonable with BT and RF, with slightly better performance by RF as assessed by Kappa statistics, correlation estimates, and spatial distribution of importance values. Although RTA did not perform as well as BT and RF, it provided interpretive models for species whose distributions were captured well by our current set of predictors. MARS was adequate for predicting current distributions but unacceptable for future climate. We consider RTA, BT, and RF modeling approaches, especially when used together to take advantage of their individual strengths, to be robust for predictive mapping and recommend their inclusion in the ecological toolbox.