区域生态承载力与可持续发展

本文探讨了生态承载力的概念和估测的方法,分析了大陆典型生态系统净第一性生产力的背景值,研究了确定自然系统最优生态承载力的依据,在此基础上,提出了提高区域生态承载力,实现区域可持续发展基本对策。     

Importance of movement constraints in habitat selection studies

The aim of this study is to empirically illustrate the importance of taking movement constraints into account when testing for habitat selection with telemetry data. Global Positioning System relocations of two Scandinavian brown bears were used to compare the results of two different tests of habitat selection by the bears within their home range. Both relied on the comparison of observed dataset with datasets simulated under the hypothesis of random habitat use. The first analysis did not take movement constraints into account (simulations were carried out by randomly distributing a set of points in the home range) whereas the second analysis accounted for these constraints (simulations were carried out by building random trajectories in the home range). The results for the two analyses showed contrasted results. Therefore, not accounting for movement constraints in analyses may result in a misleading biological interpretation. Autocorrelation between relocations is not undesirable: it contains information about ecological processes that should be integrated in habitat selection analyses.     

西南地区陆地植被生态系统NPP时空演变及驱动力分析

研究西南地区陆地植被生态系统净初级生产力(NPP)的时空演变特征及其驱动力,对区域生态环境保护具有重要的现实意义.利用2000～2021年MODIS NPP、1999～2021年基于站点的气象数据和2000～2020年土地利用类型等数据,结合主成分分析、残差分析、Theil-Sen Median趋势分析和偏相关分析等方法,研究西南地区陆地植被生态系统NPP时空演变及其对驱动力的响应特征.结果表明,时间上,2000～2021年西南地区植被NPP呈波动上升趋势,速率为3.54g·(m²·a)^-1.气候变化和人类活动影响下,农田、草地和森林生态系统NPP均呈上升趋势,但农田生态系统NPP的上升趋势最为显著.空间上,西南地区植被NPP呈上升趋势的面积占比为89.06%,显著上升和极显著上升的区域主要分布在广西南部、四川东部、重庆西部,以及云南和贵州交界处.气候变化和人类活动对西南地区植被生长具有双重影响,气候变化和人类活动影响下农田生态系统NPP呈上升趋势的面积占比均高于草地和森林生态系统.西南地区植被NPP与各气象因子的相关性呈明显地域差异.区域尺...     

气候变化和人类活动对我国典型草原区植被恢复的影响

草原区作为影响我国陆地生态系统碳水循环和生态安全的重点区域,对气候变化和人类活动极为敏感.然而,有关气候变化和人类活动对该区域植被恢复相对贡献的认识尚存分歧.以生态系统净初级生产力(NPP)为评价指标,通过对比MODIS观测的实际NPP和基于Thornthwaite Memorial模型估算的潜在NPP的趋势差异,量化了2000～2020年气候变化和人类活动对我国典型草原区(北方温性草原区和青藏高原高寒草原区)植被恢复的相对作用.结果表明,研究区内93%的草地植被呈恢复趋势,NPP平均增加速率达(以C计)2.12 g·(m²·a)^-1(P<0.01),其中,近一半植被恢复区受气候变化和人为活动共同控制,约36%和10%植被恢复区分别受气候变化和人类活动的独立控制;此外,不同草地类型气候变化主导植被恢复的面积占比差异大,主要表现为高寒草地明显高于温性草地,气候条件越干旱,气候主导面积占比越大.人类活动不是北方温性草原区和青藏高原高寒草原区植被恢复的主要原因,但在气候条件恶化地区,人类活动可降低甚至抵消气候变化对植被的负面影响.未来需加强长...     

气候变化背景下人类活动对承德接坝区植被净初级生产力的影响

承德接坝区位于农牧过渡区,对气候变化和人类活动极为敏感.以植被净初级生产力(NPP)作为评价指标,基于Thornthwaite Memorial模型计算潜在NPP和MODIS NPP遥感影像获取实际NPP数据,利用潜在NPP与实际NPP间的差值衡量人类活动作用下NPP的大小,运用Slope趋势和变异系数法分析实际NPP、潜在NPP和人类活动作用下NPP的变化趋势及稳定性分布,并采用相关系数法分析实际NPP与年降水量和年均气温间的相关性,最终量化气候变化和人类活动对该区域植被变化的影响.结果表明,潜在NPP自西北向东南递增,其变化趋势和稳定性均为自西向东递增.实际NPP与年降水量和年均气温呈正相关区域面积占比分别是99.87%和91.66%.该区域99.85%的植被得到改善且变化稳定,主要是由气候因素和人类活动共同主导(99.71%),而植被退化完全是由人为因素所导致(0.15%).     

河流-水库体系水体表层pCO_2时空变化特征及其扩散通量——以六冲河、洪家渡水库、红枫湖为例

为了了解筑坝后河流-水库体系CO2的释放特征,本研究于2011年5月至2012年5月调查了乌江流域的六冲河以及处于不同营养水平的洪家渡水库和红枫湖表层水体的基本物理、化学和生物参数及溶解无机碳碳同位素组成(δ13 CDIC),分析计算表层水体pCO2及水气界面的CO2交换通量(CO2flux)。六冲河、洪家渡水库和红枫湖表层水体的pCO2年平均值分别为684±195μatm、884±484μatm和592±596μatm,均大于大气中的pCO2,表现为大气CO2的源。红枫湖表层水体中的pCO2受到藻类等水生生物的作用最强,洪家渡水库次之,六冲河表层水体最弱,导致它们表层水体中的pCO2具有明显不同的时间分布特征,相应的CO2flux的时间分布也明显不同。红枫湖的初级生产力水平最高,在2011年5月至2011年9月时表现为大气CO2的汇,水气界面的交换通量平均为-5.70mmol.m-2.d-1。δ13 CDIC和pCO2同步变化揭示了水体表层pCO2时空变化受藻类等生物活动的影响。本研究表明,高频率的监测对于准确评估河流-水库体系CO2flux非常必要。     

中国八大经济区GPP变化及影响因子协同机制

以中国和八大综合经济区为研究区,全面分析人文因子、土地利用类型、气候因子和地形因子对植被总初级生产力(GPP)空间分异的影响差异.利用MODIS GPP数据、气象数据、土地利用类型、DEM数据、夜间灯光和人口密度数据等,基于Theil-Sen Median趋势分析、Mann-Kendall显著性检验和地理探测器模型,在全国和经济区尺度上分析2000～2020年植被GPP时空变化特征,探测植被空间分异的影响因子及影响因子间的协同机制.结果表明,2000～2020年中国及八大经济区植被GPP整体呈波动上升趋势,呈上升趋势的区域占总面积的84.46%,其中,呈极显著上升区域占19.86%,主要分布在黄河中游综合经济区中部和大西北综合经济区东部.影响因子探测结果表明,湿度、日照时数、降水和土地利用类型是中国植被GPP空间分异的主要影响因子,其中,湿度的影响力最高,q值为0.64.经济区尺度上,湿度、日照时数、降水是影响东北、黄河中游、大西南和大西北综合经济区植被GPP空间分异的主导因子,而人文因子对东部和南部沿海综合经济区植被空间分异的影响较大.交互作用探测结果表明,中国植被GPP空间分异主要...     

沙尘和灰霾期间中国近海大气氮沉降通量估算

利用WRF-CMAQ模型估算了2011年4月26日~5月3日沙尘事件和2016年1月1日~7日灰霾事件在中国近海引起的总无机氮（TIN）沉降通量,分析了两类事件对中国近海海洋初级生产力的贡献,探究了灰霾事件时中国近海无机氮沉降的可能来源.结果表明：典型沙尘和灰霾过程中,中国近海TIN干沉降通量均值分别为6.77,3.01mg N/（m²·d）,是晴朗天的6.84,3.04倍,可提供38.5,17.1mg C/（m²·d）的海洋初级生产力;沙尘期间无机氮沉降以还原态氮（NH₃和铵态氮,占TIN的62.2%）为主,灰霾期间则以氧化态氮（HNO₃和硝态氮,占TIN的84.3%）为主.典型沙尘事件TIN沉降对黄海影响最大,对渤海影响最小;典型灰霾事件对黄海和东海TIN沉降影响相当,高于对渤海的贡献.典型灰霾期间,京津冀和山东西北部（区域一）对渤、黄、东海TIN干沉降的贡献均大于长江三角洲及周边地区（区域二）,区域一对3个海域TIN干沉降的贡献约为38.7%~74.6%,区域二的贡献约为1.14%~12.1%.     

Seasonal dependency of controlling factors on the phytoplankton production in Taihu Lake, China

In this study, 44 profiles of gross primary productivity(GPP) and sunlight, along with water temperature, Chlorophyll-a(Chla) and nutrients, were observed in Meiliang Bay of Taihu Lake, China, in the spring, summer, and fall seasons. Effects of water temperature, light,and nutrient concentration were examined in relation to the GPP-unit-Chla(GPP of algae per Chla). The results showed that the optimum temperature for the GPP of phytoplankton was 27.9°C, the optimal PNA-unit-Chla(photon number absorbed by phytoplankton per Chla) was 0.25(mol), and the HSCN-unit-Chla and HSCP-unit-Chla(half-saturation constants of nitrogen and phosphorus of algae per Chla) were 0.005(mg/L) and 0.0004(mg/L), respectively. The seasonal dependency of the effect of different factors on the GPP was analyzed. Compared with temperature and nutrients, light was found to be the most important factor affecting the GPP during the three seasons. The effect of temperature and nutrients on the GPP of phytoplankton has obvious seasonal change. In spring, temperature was the secondary factor affecting the GPP of phytoplankton, and the effect of nutrients may be negligible in the eutrophic lake on account of temperature limit, which showed that the GPP of algae was only affected by the physical process. In summer and fall, temperature didn't affect the GPP of algae, and the presence of nutrients was the secondary factor affecting the GPP of phytoplankton. From summer to fall, effect of phosphorus was weakened and effect of nitrogen was enhanced.     

Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison

Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among CBM-CFS3 and four process models (ecosys, CN-CLASS, Can-IBIS and 3PG) over a 2500 ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (NPP), net ecosystem productivity (NEP) and net biome productivity (NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled NBP among models was much smaller, and attributed mainly to differences in simulated NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual NPP and NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July-September (T_amax) in all process models (R² = 0.4-0.6), indicating that these correlations were robust. The negative correlation between T_amax and NEP was attributed to different processes in different models, which were tested by comparing CO₂ fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (T_a). The general agreement in sensitivity of annual NPP to T_amax among the process models led to the development of a generalized algorithm for weather effects on NPP of coastal temperate coniferous forests for use in inventory-based models such as CBM-CFS3: NPP′ = NPP − 57.1 (T_amax − 18.6), where NPP and NPP′ are the current and temperature-adjusted annual NPP estimates from the inventory-based model, 18.6 is the long-term mean daily maximum air temperature during July-September, and T_amax is the mean value for the current year. Our analysis indicated that the sensitivity of NPP to T_amax was nonlinear, so that this algorithm should not be extrapolated beyond the conditions of this study. However the process-based methodology to estimate weather effects on NPP and NEP developed in this study is widely applicable to other forest types and may be adopted for other inventory based forest carbon cycle models.