Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region,Türkiye

Above-ground biomass (AGB) is an important component for identifying carbon stocks, monitoring the impacts of climate change, and evaluating merchantable timber. Accurate prediction of forest AGB is central to the correct interpretation of these components and to produce usable data for planners and researchers. In this study, remotely sensed time-series data derived from Landsat 8 (reflectance (R) and vegetation indices (VI)), topographic (T) and climate (C) data were used as independent variables to predict AGB of pure Calabrian pine (Pinus brutia Ten.) stands using multiple regression analysis (MLR) and support vector machines (SVM) methods. The AGB modeling was done by using independent variables individually and by combining variables, and the AGB maps of the most successful models obtained from MLR and SVM methods were produced. It was determined that the most successful variable group was the VI when the independent variables were used one by one (MLR Training R² = 0.50, SVM Training R² = 0.67). The most successful predictions in AGB modeling were obtained with combining all independent variables and using the SVM method (Training R² = 0.85, Validation R² = 0.69). In the combination of independent variables, VI and C data made the greatest contribution to the success of the AGB prediction. The ‘green leaf index’ vegetation indices had the most significant effect on the modeling AGB. In this study, T and C in addition to spectral data has increased the AGB estimation performance. It has been found that the SVM method yielded higher model accuracy than MLR method in predicting AGB. Overall, the spectral data and the SVM method can contribute to improving the accuracy of AGB estimates and provide an effective approach towards the capability for forest ecosystem monitoring.     

Vegetative and reproductive phenology of the mangrove Bruguiera gymnorrhiza (L.) Lam. on Okinawa Island, Japan

Vegetative and reproductive phenology of the subtropical mangrove Bruguiera gymnorrhiza was investigated at Manko Wetland, Okinawa Island, Japan. Phenology was assessed using litterfall data over 4 years. Leaf and stipule litterfall occurred throughout the year, with distinct seasonal patterns. Kendall’s coefficient of concordance, W, revealed that the monthly changes in leaf and stipule litterfall were strongly and significantly concordant among years. Leaf litterfall was linked to monthly day length and maximum wind speed, and stipule litterfall was linked to monthly mean air temperature, monthly rainfall, and maximum wind speed. Branch litterfall showed no clear monthly pattern, but tended to vary with monthly maximum wind speed. Mean leaf longevity was 19 months. Mean total litterfall was 10.1 Mg ha^?1 year^?1, with the largest component being vegetative organs (58 %). Flower and propagule litterfall were highest in autumn and summer, respectively, and lowest in winter. The W values revealed that, of the reproductive organs, only flower litterfall had a significant monthly trend. Flower litterfall was correlated with monthly mean air temperature and relative humidity. The average development periods from flower buds to flowers and from flowers to mature propagules were approximately 1 and 8 months, respectively. It took 9 months to produce mature propagules from flower buds. Except for branches, all vegetative and reproductive components of litterfall had clear annual cycles.     

Phenological traits of the mangrove Rhizophora stylosa Griff. at the northern limit of its biogeographical distribution

The vegetative and reproductive phenology of the subtropical mangrove species Rhizophora stylosa was investigated at Manko Wetland, Okinawa Island, Japan. We assessed phenology using litterfall data over four years. Leaf and stipule litterfall occurred throughout the year, with distinct seasonal patterns. Kendall’s coefficient of concordance, W, revealed that the monthly changes in leaf and stipule litterfall were strongly and significantly concordant among years. Leaf litterfall was significantly correlated with monthly maximum wind speed and monthly day length, and stipule litterfall was significantly correlated with monthly mean air temperature and relative humidity. Branch litterfall showed no clear monthly pattern and correlated well with monthly maximum wind speed. Mean total litterfall was 11.2 Mg ha^?1 year^?1, with the largest component being vegetative organs (78.7 %). Flower, fruit, and propagule litterfall were all highest in summer and lowest in winter. The W values revealed that most reproductive organs in litterfall had significant monthly trends. Flower and fruit litterfall were significantly correlated with monthly day length and monthly mean air temperature, respectively. The average development periods from flower buds to flowers, fruits, and mature propagules were approximately 2–3 months, 4–5 months, and 11–12 months, respectively. Except for branches, all vegetative and reproductive components of litterfall had approximately one year cycles.     

联合GF-6和Sentinel-2红边波段的森林地上生物量反演

光谱反射率能反映地物差异,是森林地上生物量（Aboveground Biomass,AGB）遥感反演的理论基础。红边波段处于近红外与红光波段交界处快速变化的区域,能对植被冠层结构和叶绿素含量的微小变化做出快速反应,对植被生长状况较敏感。研究以GF-6和Sentinel-2多光谱影像作为数据源,结合野外调查AGB数据,构建落叶松和樟子松AGB线性和非线性估测模型,通过比较模型精度选择最优模型进行森林AGB反演和空间分布制图。结果表明：GF-6和Sentinel-2影像红边波段反射率与落叶松、樟子松AGB均呈显著相关（P<0.05）,红边波段对AGB估测较敏感。多变量估测模型整体估测效果优于单变量模型,所有模型中多元线性回归模型取得了最优的决定系数（落叶松R²=0.66,樟子松R²=0.65）和最低的均方根误差（落叶松RMSE=31.45 t/hm²,樟子松RMSE=54.77 t/hm²）。相比单个数据源,联合GF-6和Sentinel-2影像构建的多元线性回归模型估测效果得到了显著提升,模型RMSE对于落叶松和樟子松AGB估测分别最大降低了22.9%和11.2%。增加红边波段进行AGB估测能显著提高模型估测精度,三组数据源分别加入红边波段信息后进行建模,模型RMSE得到了显著降低。GF-6拥有800 km观测幅宽和高效的重访周期,可以快速地提供大尺度时间序列数据,在森林地上生物量反演和动态监测方面有着很大潜力。     

冰雪灾害对中亚热带人工针叶林净初级生产力的影响

结合中亚热带江西千烟洲人工针叶林2005、2008和2011年3次树木清查数据以及树木相对生长方程,比较了2008年1月南方冰雪灾害前后的NPP,评价了森林生态系统灾后的恢复能力。结果表明:乡土树种马尾松(Pinus massoniana)与杉木(Cunninghamia lanceolata)比外来树种湿地松(Pinus elliottii)抗灾害能力强;在个体水平上,胸径(D)较大的树木抗灾害能力较差。灾后马尾松与杉木的D增长率降低,而湿地松增大。冰雪灾害导致大量碳(10.44 t C/hm2)从乔木层碳库转移到死生物量碳库,占乔木层碳储量的18.28%。灾前NPP和碳利用效率(CUE)分别为736.23 g C m-2a-1和0.41;灾后经过近4年的恢复,NPP和CUE分别为683.08 g C m-2a-1和0.38。     

长白山阔叶红松林生产力随林分发育的变化

林龄是影响森林生态系统碳储量和碳通量的一个关键因子。量化森林生产力随林分发育的变化规律,对于优化林龄结构,促进资源利用最大化,更好地发挥森林在调节CO₂吸收、储存和释放中的作用十分重要。本研究采用空间代替时间法,在露水河林区设置12块不同发育阶段的阔叶红松林样地,运用经本地参数化的Biome-BGC模型,模拟了阔叶红松林净初级生产力(NPP)随林分发育的动态变化,分析了阔叶红松林NPP在4种发育情景模式下随林分发育的变化规律。结果表明: 不同龄组阔叶红松林的生物量表现为幼龄林<中龄林<成熟林<过熟林,其平均生物量分别为(224.35±20.68)、(237.23±39.96)、(259.16±19.51)和(357.57±84.74) t·hm^-2。模型模拟的不同发育阶段阔叶红松林NPP的变化范围为489.8～588 g C·m^-2·a^-1,模拟结果与MODIS观测结果有较好的一致性,反映了Biome-BGC模型模拟阔叶红松林碳通量的合理性和准确性。模型模拟的阔叶红松林NPP随林分发育呈先增加后下降的变化趋势,在中龄林时达到最高,过熟林时最低。4种阔叶红松林发育动态情景模式下的NPP变化特征表明,在起始状态为人工种植红松林的自然发育和发育过程中对阔叶树实施采伐控制的两种情景下,成熟林时期NPP最高;而在起始状态为天然次生白桦林的自然发育和经人为采伐动态控制的两种情景下,均表现为幼龄期NPP最高。     

基于多光谱卫星影像与机器学习算法的松材线虫病受害林分识别研究

松材线虫病(Pine Wilt Disease, PWD)被称为“松树癌症”,具有高传染率和高死亡率,对我国森林资源构成了严重的威胁,对我国的经济、社会和生态造成了重大损失。及时发现并清理疫木是遏制松材线虫病蔓延的有效手段,精准监测疫木是防控松材线虫病的前提,但是现阶段缺少大面积识别松材线虫病疫木的技术方法。本文旨在探索哨兵-2号与Landsat-8遥感卫星影像对受害松林的识别能力,采用随机森林(Random Forest, RF)、支持向量机(Support Vector Machine, SVM)、决策树(Decision Tree, DT)和极端梯度提升(Extreme Gradient Boosting, XGBoost)等4种机器学习算法建立了松材线虫病监测模型。结果表明：基于哨兵-2号影像数据建立的监测模型对受害松林的识别准确率高于Landsat-8遥感卫星影像,其中基于10 m分辨率的影像数据建立的监测模型识别准确率最高,随机森林、决策树、支持向量机和极端梯度提升等算法建立模型的准确率分别达到了79.3%、76.2%、78.7%和78.9%。在3种不同的影像数据集中,RF...     

The influence of black locust (Robinia pseudoacacia) flower and leaf fall on soil phosphate

To investigate the influence of black locust (Robinia pseudoacacia) flower and leaf fall on soil phosphate, we monitored litterfall, litter decomposition, and soil membrane phosphate in a R. pseudoacacia forest on Mt. Ilzasan, Seoul, Korea. R. pseudoacacia flower litter was 30–50% of total litter production in May and the flowers decomposed rapidly. More than 11% of R. pseudoacacia leaf litter decomposed from February to May, while that of Quercus spp. decomposed very little. Fast decomposition of R. pseudoacacia flower and leaf litter significantly increased membrane phosphate in the soil. The rapid nutrient-cycling of R. pseudoacacia through flower litterfall and rapid decomposition benefits the plant itself in the growing season when nutrients demand is increasing. Rapid nutrient-cycling might be a strategy that helps R. pseudoacacia to persist in poor soil environments.     

择伐对阔叶红松林碳密度和净初级生产力的影响

准确量化森林碳密度和净初级生产力(NPP)对于评价森林生态系统在全球碳循环中的作用至关重要.本研究以小兴安岭原始阔叶红松林和择伐(择伐强度30%,择伐对象为大径级红松)34年后的阔叶红松林为对象,采用样地清查和异速生长方程法测定了不同林分的碳密度和NPP.结果表明: 原始林和择伐林的碳密度总量分别为(397.95±93.82)和(355.61±59.37) t C·hm^-2,其中植被碳密度、碎屑碳密度、土壤碳密度分别占总碳库的31.0%、3.1%、65.9%和31.7%、2.9%、65.4%,两者的总碳密度和各组分的分配比例均无显著差异. 原始林和择伐林的NPP总量分别为(6.27±0.36 )和(6.35±0.70) t C·hm^-2·a^-1,乔木层、灌木和草本层、细根所占比例分别为60.3%、2.0%、37.7%和66.1%、2.0%、31.2%,两者的总NPP和各组分的贡献率均无显著差异.而原始林和择伐林中针、阔叶的NPP比例分别为4724∶52.76和20.48∶79.52,两者差异显著.择伐34年后阔叶红松林的碳密度和NPP均达到了择伐前水平.     

Litterfall Production Along Successional and Altitudinal Gradients of Subtropical Monsoon Evergreen Broadleaved Forests in Guangdong, China

Evaluation of litterfall production is important for understanding nutrient cycling, forest growth, successional pathways, and interactions with environmental variables in forest ecosystems. Litterfall was intensively studied during the period of 1982–2001 in two subtropical monsoon vegetation gradients in the Dinghushan Biosphere Reserve, Guangdong Province, China. The two gradients include: (1) a successional gradient composed of pine forest (PF), mixed pine and broadleaved forest (MF) and monsoon evergreen broadleaved forest (BF), and (2) an altitudinal gradient composed of Baiyunci ravine rain forest (BRF), Qingyunci ravine rain forest (QRF), BF and mountainous evergreen broadleaved forest (MMF). Mean annual litterfall production was 356, 861 and 849 g m⁻² for PF, MF and BF of the successional gradient, and 1016, 1061, 849 and 489 g m⁻² for BRF, QRF, BF and MMF of the altitudinal gradient, respectively. As expected, mean annual litterfall of the pioneer forest PF was the lowest, but rapidly increased over the observation period while those in other forests were relatively stable, confirming that forest litterfall production is closely related to successional stages and growth patterns. Leaf proportions of total litterfall in PF, MF, BF, BRF, QRF and MMF were 76.4%, 68.4%, 56.8%, 55.7%, 57.6% and 69.2%, respectively, which were consistent with the results from studies in other evergreen broadleaved forests. Our analysis on litterfall monthly distributions indicated that litterfall production was much higher during the period of April to September compared to other months for all studied forest types. Although there were significant impacts of some climate variables (maximum and effective temperatures) on litterfall production in some of the studied forests, the mechanisms of how climate factors (temperature and rainfall) interactively affect litterfall await further study.     

鼎湖山3种演替群落凋落物及其水分特征及对比研究

对鼎湖山3种演替群落凋落物及其水分特征研究表明,凋落物现存量为针叶林＞混交林＞阔叶林,年凋落量阔叶林＞混交林＞针叶林,说明针叶林凋落物分解较阔叶林迟缓,针叶林凋落物中叶所占的比例最大,而阔叶林最小,枝和花果所占的比例以阔叶林最大,针叶林最小,与它们的林木分枝多少以及林冠幅度大小有关,凋落物的最大持水率为针叶林＞混交林＞阔叶林,但差异不明显,凋落物含水量以阔叶林最大,混交林次之,针叶林最小,与凋落物最大持水率恰恰相反,说明凋落物的含水量受林地环境条件的制约,凋落物饱和含水时相对自由水面蒸发率阔叶林为78．95％,混交林为82．45％,针叶林为91．22％,说明在相同的环境条件下,阔叶林凋落物水分损失较难,而针叶林凋落物水分损失较容易,这也是阔叶林具有较小的最大持水量而却有较大叶林凋落物态含水量的原因之一。     

Litter production,nutrient recycling and litter accumulation inPinus caribaea Morelet var.hondurensis stands in the northern Guniea savanna of Nigeria

Summary The seasonal pattern and quantity of litterfall were studied during a two-year period in two unthinned stands ofPinus caribaea Morelet var. hondurensis Barr. and Golf. in Nigeria. Although pine needles were cast continuously throughout the year, the peak period of litterfall occurred in the dry months of March and April. Mean values of annual litterfall were 3068 and 3665 kg/ha in the two stands aged 7–9 and 9–11 years respectively. Nutrient returns in litterfall in the stands had mean values of 15.0, 0.6, 17.3, 18.2 and 6.3 kg/ha of N, P, K, Ca and Mg respectively. Comparatively low amounts of N and P returned in litterfall were attributed to soil deficiencies of the two elements.Measurements of ground litter showed considerable dry matter accumulation (11378 kg/ha) in the litter layers. Estimates of litter decomposition rate and recycling time showed that it would take 3 to 4 years for the organic matter in annual litterfall to decompose completely as contrasted to about 2 to 5 months often reported under mixed nautral savanna vegetation in the same climatic environment. Similar estimates of nutrient recycling time also showed that between 2 to 4 years were required to mineralize nutrient elements in the annual litterfall; the relative mobilities of the elements were in the order K>Mg>P>NCa.     

Spatiotemporal patterns and drivers of net primary production in the terrestrial ecosystem of the Dajiuhu Basin,China, between 1990 and 2018

Net primary production (NPP) plays a vital role in both the evolution of ecosystems and the terrestrial carbon cycle and is influenced by geographical conditions and climate change. Understanding the terrestrial carbon balance requires an in-depth knowledge of the relationships between NPP and geographical and climatic conditions. This study aimed to simulate and map the daily spatiotemporal features of terrestrial NPP in the Dajiuhu Basin (DB), China, using the BEPS-TerrainLab V2.0 model. This area is highly sensitive to climate change and is a water source in the central path of the South-to-North Water Transfer Project. Changes in the distribution of daily and seasonal NPP between 1990 and 2018 were examined using the Mann-Kendall (MK) test, the moving t-test (MTT), and multiple regression analyses. It was found that: 1) The model explained 79% of the variation in eddy covariance (EC)-tower-measured NPP, and could thus be applied to the DB; 2) The mean annual NPP in the DB between 1990 and 2018 was 705 g C/m²/yr, with the terrestrial NPP decreasing before 1999 (−31.8 g C/m²/yr) and increasing after 1999 (0.87 g C/m²/yr); 3) The NPP first increased and then decreased with increasing altitude, with higher NPP values mainly found in the mountains on the periphery of the basin and lower NPP values in the central basin;4) Changes in NPP during autumn and summer contributed the most to the annual NPP trend. Temperature and NPP were positively correlated in summer and autumn, whereas they were negatively correlated in spring and winter. Precipitation and NPP were positively correlated in spring, autumn, and winter; 5) The sensitivities of NPP to temperature and precipitation differed across the different seasons. The sensitivities of the annual NPP to temperature and precipitation decreased and increased, respectively, compared with those before 1999. Although the contribution of precipitation to the NPP trend became more significant after 1999, that of temperature decreased. This study proposes an approach for a detailed study of daily changes in NPP and for examining the link between environmental factors, climatic conditions, and NPP distribution.     

Mapping land cover and forest density in Zagros forests of Khuzestan province in Iran: A study based on Sentinel-2, Google Earth and field data

Zagros forests in western Iran have widely been destroyed because of various reasons. This study was performed to provide the land cover and forest density maps in Zagros forests of Khuzestan province using Sentinel-2, Google Earth and field data. The forest boundary in Khuzestan province was digitized in Google Earth. Sentinel-2 satellite images were provided for the study area. One 1:25000 index sheet of Iranian Mapping Organization (IMO) was selected as pilot area in the province. Sentinel-2 image of the pilot area was classified using different supervised classification algorithms to select the best algorithm for land cover mapping in Khuzestan province. In addition, to evaluate the accuracy of Google Earth data, field sampling was performed using random plots in different land covers. Field data of forest plots were applied to investigate tree canopy cover percent (forest density), as well. Classification of Sentinel-2 image in Zagros area of Khuzestan province was done using the best algorithm and the land cover was obtained. The forest density map was also obtained using a linear regression model between tree canopy cover percent (obtained from field plots) and normalized difference vegetation index (NDVI) (obtained from NDVI map). Finally, the accuracy of land cover map was assessed by some square plots on Google Earth. Results demonstrated that support vector machine (SVM) algorithm had the highest accuracy for land cover mapping. Results also showed that Google Earth images had a good accuracy in the Zagros forests of Khuzestan province. Results demonstrated that NDVI has been a good predicator to estimate tree canopy cover in the study area. Based on results, an area of 443,091.22 ha is covered by Zagros forests in Khuzestan province. Results of accuracy assessment of the land cover map showed the good accuracy of this map in Khuzestan province (overall accuracy: 91% and kappa index: 0.83). For optimum management of Zagros forests, it is suggested that the land cover and forest density mapping will be performed using SVM algorithm, NDVI, and Sentinel-2 satellite images in Zagros forests of Khuzestan province in the certain periods.     

Carbon dynamics,net primary productivity and human‐appropriated net primary productivity across a forest–cocoa farm landscape in West Africa

Terrestrial net primary productivity (NPP) is an important metric of ecosystem functioning; however, there are little empirical data on the NPP of human‐modified ecosystems, particularly smallholder, perennial crops like cocoa (Theobroma cacao), which are extensive across the tropics. Human‐appropriated NPP (HANPP) is a measure of the proportion of a natural system's NPP that has either been reduced through land‐use change or harvested directly and, previously, has been calculated to estimate the scale of the human impact on the biosphere. Additionally, human modification can create shifts in NPP allocation and decomposition, with concomitant impacts on the carbon cycle. This study presents the results of 3 years of intensive monitoring of forest and smallholder cocoa farms across disturbance, management intensity, distance from forest and farm age gradients. We measured among the highest reported NPP values in tropical forest, 17.57 ± 2.1 and 17.7 ± 1.6 Mg C ha^?1 year^?1 for intact and logged forest, respectively; however, the average NPP of cocoa farms was still higher, 18.8 ± 2.5 Mg C ha^?1 year^?1, which we found was driven by cocoa pod production. We found a dramatic shift in litterfall residence times, where cocoa leaves decomposed more slowly than forest leaves and shade tree litterfall decomposed considerably faster, indicating significant changes in rates of nutrient cycling. The average HANPP value for all cocoa farms was 2.1 ± 1.1 Mg C ha^?1 year^?1; however, depending on the density of shade trees, it ranged from ?4.6 to 5.2 Mg C ha^?1 year^?1. Therefore, rather than being related to cocoa yield, HANPP was reduced by maintaining higher shade levels. Across our monitored farms, 18.9% of farm NPP was harvested (i.e., whole cocoa pods) and only 1.1% (i.e., cocoa beans) was removed from the system, suggesting that the scale of HANPP in smallholder cocoa agroforestry systems is relatively small.     

TEX101, a germ cell-marker glycoprotein, is associated with lymphocyte antigen 6 complex locus k within the mouse testis

In adult male mice, the glycosylphosphatidyl inositol-anchored glycoprotein TEX101 is expressed only in germ cells and is thought to be involved in spermatogenesis. However, the details regarding the function of TEX101 remain to be clarified. We previously identified Ly6k as a candidate TEX101-associated protein, but as molecular probes are not currently available to detect Ly6k, we do not have conclusive evidence of the association between TEX101 and Ly6k. In this study, we confirmed the biological interaction between TEX101 and Ly6k using an established anti-mouse Ly6k polyclonal antibody (pAb). A combination of immunoprecipitation, Western blot, and immunohistochemical analyses using the pAb revealed that TEX101 is physically associated with Ly6k within the testis. In addition, these proteins simultaneously co-migrate into the detergent-resistant membrane fractions, suggesting that TEX101 collaborates with Ly6k on the cell membrane and may play a role in spermatogenesis.     

Hierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabwe

We develop and present a novel Bayesian hierarchical geostatistical model for the prediction of plantation forest carbon stock (C stock) in the eastern highlands of Zimbabwe using multispectral Landsat-8 and Sentinel-2 remotely sensed data. Specifically, we adopt a Bayesian hierarchical methodology encompassing a model-based inferential framework making use of efficient Markov Chain Monte Carlo (MCMC) techniques for assessing model input parameters. Our proposed hierarchical modelling framework evaluates the influence of two but related covariate information sources in C stock prediction in order to build sustainable capacity on carbon reporting and monitoring. The perceived improvements in the spectral and spatial properties of Landsat-8 and Sentinel-2 data and their potential to predict C stock with shorter uncertainty bounds is tested in the developed hierarchical Bayesian models. We utilized the Mean Squared Shortest Distance (MSSD) as the objective function for optimization of sampling locations for equal area coverage. Specifically, we evaluated the models using four selected remotely sensed vegetation indices namely, the normalised difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and an additional distance to settlements anthropogenic variable that justifies from the history of the studied plantation forest in the eastern highlands of Zimbabwe. We evaluated two models making use of Landsat-8 and Sentinel-2 derived predictors using the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Coverage (CVG) and Deviance Information Criteria (DIC). The Sentinel-2 based C stock model resulted in RMSE of 1.16 MgCha⁻¹, MAE of 1.11 MgCha⁻¹, CVG of 94.7% and a DIC of −554.7 whilst its Landsat-8 based C stock counterpart yielded a RMSE, MAE, CVG and DIC of 2.69 MgCha⁻¹, 1.77 MgCha⁻¹, 85.4% and 43.1 respectively. Although predictive models from both sensors show great improvement in predictive accuracy when modelling the spatial random effects, the Sentinel-2 based C stock predictive model substantially outperforms its Landsat-8 based C stock counterpart. The Sentinel-2 based C stock predictive hierarchical model therefore adequately addresses multiple sources of uncertainty inherent in the spatial prediction of C stock in disturbed plantation ecosystems. It is evident from the results of this study that carbon reporting and monitoring can always be improved by scouting for improved and easily accessible remote sensing data and allow forest practitioners to keep track of error across space in resource environments of interest.     

Changes in Production and Nutrient Cycling across a Wetness Gradient within a Floodplain Forest

Floodplain forest ecosystems are highly valuable to society because of their potential for water quality improvement and vegetation productivity, among many other functions. Previous studies have indicated that hydrology influences productivity but that the relationship between hydroperiod and productivity is a complex one. Consequently, we compared multiple indexes of productivity, nutrient circulation, and hydroperiod among three communities on the Flint River floodplain, Georgia, that differed in terms of inundation frequency. We hypothesized that (a) the wettest community would have the lowest total net primary production (NPP) values because of saturated soil conditions; (b) as wetness increases, nutrient circulation in litterfall would decrease because of the hypothesized lower productivity in the wetter community; and (c) as wetness increases, internal translocation would become more efficient. The study site was partitioned into three wetness types—somewhat poorly drained (SPD), intermediate (I) and poorly drained (PD). We found that belowground biomass was greatest on the SPD, litterfall was similar for all three sites, and that woody biomass current annual increment (CAI) was greatest in the PD community. However, when the three variables were totaled for each site, the PD had the greatest NPP, thus disproving hypothesis (a). For hypothesis (b), we observed that P content in litterfall, although not significant, followed the predicted trend; nitrogen (N) content displayed the opposite pattern (PD > I > SPD). As wetness increased, internal translocation became more efficient for phosphorus (support for hypothesis [c]), but the SPD community was more efficient at retranslocating N (contradiction of hypothesis [c]). Received 19 June 2000; accepted 19 October 2000.     

Long-term needle litterfall of a Scots pine Pinus sylvestris stand: relation to temperature factors

Summary Needle litterfall of a Scots pine was caught over 24 years (1962–1986) with litter-traps in a Scots pine stand in southeastern Finland. The age of the trees averaged 111 years in 1962. The stand was naturally recruited and only minor silvicultural treatments occurred during its history. Litterfall showed great year-to-year variation, the minimum being 18 g/m² (in 1968) and maximum 213 g/m² (in 1973). There was no overall trend in the amount of litterfall, and the age of the stand was thus not important in determining the needle fall. We used time domain time series analysis (ARIMA) and standard climatic data (temperature, precipitation) to investigate the relationship of litterfall to climatic factors. Mean July temperature was clearly correlated with needle litterfall. High temperature in July coincided with enhanced litterfall in the same and the next year. Litterfall enhanced litterfall in the same and the next year. Litterfall increased also after high temperatures during March–April, but only in the same year. In addition to these the litterfall had a 4-year self-dependency. This is approximately the same as the mean longevity of needles in the study area. Altogether the time series model we propose covers about 90% of the variance of the original time series.