Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing

In this paper we describe and test a sub-model that integrates the cycling of carbon (C), nitrogen (N) and phosphorus (P) in the Soil Water Assessment Tool (SWAT) watershed model. The core of the sub-model is a multi-layer, one-pool soil organic carbon (S_C) algorithm, in which the decomposition rate of S_C and input rate to S_C (through decomposition and humification of residues) depend on the current size of S_C. The organic N and P fluxes are coupled to that of C and depend on the available mineral N and P, and the C:N and N:P ratios of the decomposing pools. Tillage explicitly affects the soil organic matter turnover rate through tool-specific coefficients. Unlike most models, the turnover of soil organic matter does not follow first order kinetics. Each soil layer has a specific maximum capacity to accumulate C or C saturation (S_x) that depends on texture and controls the turnover rate. It is shown in an analytical solution that S_x is a parameter with major influence in the model C dynamics. Testing with a 65-yr data set from the dryland wheat growing region in Oregon shows that the model adequately simulates the S_C dynamics in the topsoil (top 0.3 m) for three different treatments. Three key model parameters, the optimal decomposition and humification rates and a factor controlling the effect of soil moisture and temperature on the decomposition rate, showed low uncertainty as determined by generalized likelihood uncertainty estimation. Nonetheless, the parameter set that provided accurate simulations in the topsoil tended to overestimate S_C in the subsoil, suggesting that a mechanism that expresses at depth might not be represented in the current sub-model structure. The explicit integration of C, N, and P fluxes allows for a more cohesive simulation of nutrient cycling in the SWAT model. The sub-model has to be tested in forestland and rangeland in addition to agricultural land, and in diverse soils with extreme properties such high or low pH, an organic horizon, or volcanic soils.     

Estimating dyad association probability under imperfect and heterogeneous detection

In animal behaviour studies, association indices estimate the proportion of time two individuals (i.e. a dyad) spend in association. In terms of dyads, all association indices can be interpreted as estimators of the probability that a dyad is associated. However, traditional indices rely on the assumptions that the probability to detect a particular individual (p) is either approximately one and/or homogeneous between associated and not associated individuals. Based on marked individuals we develop a likelihood based model to estimate the probability a dyad is associated (ψ) accounting for p < 1 and possibly varying between associated and not associated individuals. The proposed likelihood based model allows for both individual and dyadic missing observations. In addition, the model can easily be extended to incorporate covariate information for modeling p and ψ. A simulation study showed that the likelihood based model approach yield reasonably unbiased estimates, even for low and heterogeneous individual detection probabilities, while, in contrast, traditional indices showed moderate to strong biases. The application of the proposed approach is illustrated using a real data set collected from a population of Commerson's dolphin (Cephalorhynchus commersonii) in Patagonia Argentina. Finally, we discuss possible extensions of the proposed model and its applicability in animal behaviour and ecological studies.     

Ecosystems emerging. 5: Constraints

Ecosystem constraints are both ontic and epistemic. They limit activity, and as problems to be solved they drive organization, which is our hypothesis:

The driver of organization is constraint.

Solutions proliferate further constraints in an unending spiral of problem (constraint) generation and solution. As constraints proliferate, behavior narrows, and species diversify to compensate (paradox of constraint). Resource enrichment reduces constraints, releases behavior, and reduction of challenges decreases diversity (paradox of enrichment)—high diversity is expressed in low-resource environments and low diversity in high-resource environments. A three-part model of constraints is formulated for non-living systems, and also for goal-directed, problem-solving biota. Mode 1: dynamical means behavior is co-determined by internal states and external inputs. Mode 2: cybernetic employs negative feedback to keep dynamics within goal-oriented operating limits. Mode 3: model-making entails ability to represent (model) physical reality and respond to both phenomenal (modeled) and physical inputs; this property distinguishes living from nonliving systems. Principal sections of the paper elaborate dynamical constraints (three classes), boundary constraints (expressed in edge effects and trophic dynamics), physical constraints (space, time, temperature), chemical constraints (environment fitness, ecological stoichiometry, chemical evolution, limiting factors), coding constraints (environmental vs. genetic coding), network and pathway constraints (connectivity), and natural selection constraints (fitting to the biosphere). Consideration of how the world would look without constraints suggests how fundamental these are in ecosystem emergence, and how the next property in this series, differentiation, would be unmotivated without them. We conclude that constraints as a category are under-studied in ecology, poorly understood in ecological phenomenology, and (our hypothesis) comprise a ubiquitous organizing force in nature.     

Using simulation to explore the functional relationships of terrestrial carnivore population indices

Population indices based on visits to detection stations commonly are used to monitor wildlife populations. Inferences about populations are based on 1 of 2 measures: (1) change in the proportion of stations visited at least once or (2) change in the cumulative number of visits by unique individuals. The functional relationships between index responses and population density is poorly understood and can lead to misinterpretation of index data when an incorrect functional relationship (e.g. linear) is assumed. We created a flexible simulation environment to study the response of detection-based population indices under a wide variety of conditions meant to reflect species life history and study design. Proportional indices exhibited non-linear saturating responses to changes in population density while cumulative indices responded linearly. Shapes of responses were functions of home range sizes, individual detection probabilities, and spatial arrangement of animals and sampling stations. Non-linear relationships of proportional indices lead to under-estimation of mean population density when data are aggregated from multiple detection stations deployed in a heterogeneous landscape. Cumulative indices have significant statistical advantages over proportional indices including smaller sample sizes required to detect density change, linearity, consistent index responses across a wide range of densities, and ability to aggregate data to meet minimum sample size requirements. Our simulation provides a flexible tool for the interpretation of station-based population indices.     

基于氮磷指数的小流域氮磷流失风险评价

识别区域氮磷流失综合风险分布状况并对氮磷流失进行综合调控是控制非点源污染的有效措施。但传统的研究往往局限于氮或磷流失风险的单独评估和调控,以密云水库沿湖集约化农区东庄小流域为例,应用氮指数、磷指数及氮磷综合指数法,对区域氮磷流失风险进行综合评价。结果表明：流域氮、磷流失风险总体上较小,80%以上的区域均处于氮、磷流失的无风险或低风险区,但氮、磷流失的空间分布存在较大差异。其中氮流失的高度风险区集中在山地中土壤侵蚀指数较大的果园;而磷流失的高风险区域主要分布在河流沿岸的农业用地。氮磷综合风险指数显示,93.1%的区域处于无风险和低风险区,中度以上风险区占总面积的6.9%,主要集中在流域中部有着较高的肥料施用、地势陡峭且处在河流沿岸的农业用地或山地中。单独考虑氮指数或磷指数都难以反映区域氮磷流失的综合风险状况,容易忽略磷指数高氮指数低、氮指数高和磷指数低以及氮、磷风险在中等的区域。因此,在氮、磷流失风险评估基础上,进行氮磷流失风险的综合评价,可为氮磷流失的综合调控提供指导。     

细菌在近海污染监测及评价中的应用研究

通过对天津渤海湾的三个河流入海口(青静黄排水河、独流减河和海河),不同季节实地采集的水样中微生物数量及理化因子如温度、盐度、溶解氧(DO)、化学需氧量(COD)、无机氮等的研究,发现细菌指标与理化指标对污染指示结果有一定的相似性,都指示青静黄排水河河口污染较严重.利用SPSS软件的相关性分析方法对实验结果进行分析,发现水环境中细菌指标与理化因子具有一定的相关性.其中异养菌数与COD呈显著正相关性(P<0.05),与DO呈负相关性(P=0.051);大肠菌群数与理化因子DO呈极显著负相关关系(P<0.01),与COD呈正相关关系(P=0.061),与NO3-N,盐度呈负相关性(P>0.05).其他细菌指标与各理化因子也存在一定的相关性,但不明显.     

水深梯度对荆三棱(Scirpus yagara)生长的影响

研究湿地植物对水深梯度的响应对于湿地修复具有重要的指导意义.在南四湖湖边滩地上的人工修复湿地中开展水深梯度对荆三棱(Scirpus yagara)生长的影响研究,试验观测水深分别为-10、0、10、20、30 cm共5个梯度.结果显示:随着水位的升高,1)荆三棱的株高、基茎、叶长和叶宽均有所增加,当水位大于10 cm时各项指标增加更为显著;2)生物量呈增加趋势,而根茎比则逐渐减小,水位为30 cm时的生物量和根茎比分别为-10 cm时的2.67倍和58.15%;3)叶绿素含量总体呈增加趋势,但在不同水位间的差异不明显(P>0.05);4)叶绿素荧光方面,最大光化学量子产量Fv/Fm均在0.75以上,光化学淬灭qP和非光化学淬灭qN分别呈现出升高和下降的趋势.结果表明,在试验所设置的水位条件下(-10～30 cm),荆三棱的生长没有受到水深梯度增加的显著影响,相反表现出随着水深的增加而生长更好的趋势.可见,在试验设置的水深梯度范围内,相对较高的水位有利于荆三棱的生长,因此在湿地管理中应控制适当的高水位以利于其种群的生长和稳定.     

MODIS植被叶面积指数对贵州高原山地气象条件的响应

用EOS/MODIS卫星遥感分辨率为1 km的贵州2001—2008年每8 d合成植被叶面积指数（LAI）,分析了LAI的时间变化规律和年、季空间分布特征;还用贵州69个气象站的日观测数据,分别与各气象点所对应的3×3个像元LAI平均值进行气象要素（温度、降水、日照时数和水汽压）与LAI的相关分析。结果表明：该地区LAI年变化为0.3~3.0,多年平均LAI的空间分布是东南部最高（4.0）,西部和西南部最低（1.0）。显著影响LAI变化的气象因子按相关系数的大小排列是：温度、水汽压、日照时数和降水（均通过了99.9%信度检验）。水汽压对LAI产生影响的原因可能与高原喀斯特地区土壤层较薄有关。在考虑云贵高原植被年际间变化对气候变化的响应时可以把这些要素作为重要的影响因子。     

中型景观尺度下斑块类型和地理特征影响杨树人工林干部病害的发生——以河南省清丰县为例

景观病理学在开展病原扩散、病害发生及其严重程度方面以其全新的视角,为森林病害的区域控制提供了新的研究技术及理论支持。首次利用景观病理学原理和方法对河南省清丰县一个中型景观下杨树人工林干部病害发生特征开展了研究,目的是解析在大尺度下斑块类型和地理特征对杨树人工林病害发生的影响。在100 km2的调查区域,以种植方式和林分类型划分斑块类型,分析显示发病株率在不同斑块间差异显著：农田间作斑块的林木发病株率显著低于孤立斑块、纯林斑块、混交林等斑块的发病株率;但发病株率在孤立斑块、纯林斑块及混交林等斑块间无显著差异。抚育管理措施对预防和减轻杨树人工林干部病害的发生起到关键左右：精细管理林分（有修枝、施肥和锄草）的林木发病株率（p=0.001）和发病指数（p〈0.001）均显著低于粗放管理林分（无修枝、无施肥和锄草等）。人类活动,如无序修剪和放牧很可能是造成村落附近林分发病率显著高于其他地点林分的主要原因。采用logistic回归,以品种编号、树龄、树高、林分密度、林分郁闭度、林分类型、斑块类型、地理特征,等为自变量建立病害发生预测模型。方程拟合达到极显著水平（Wald=71.248,p〈0.001）。方程总的预测正确率为68.2%,发病的预测正确率为79.8%。     

基于主成分分析的云南省生态脆弱性评价

人口资源环境矛盾造成了脆弱生态环境问题突出。以典型生态脆弱区云南省为研究区,根据导致该省生态环境脆弱的自然-结果表现因素,构建科学客观地评价指标体系,运用主成分分析法进行生态脆弱性分析评价。以生态脆弱度大小为依据,对云南省16个地（州）、市生态脆弱性进行了分区,划分为极强度、强度、中度、轻度脆弱区4个区。评价结果显示,极强度、强度、中度、轻度生态脆弱区分别占全省面积的5.84%、27.84%、43.08%、23.24%。为生态脆弱性评价提供了一个可行的方法,同时应用生态脆弱性分区结果可为后续的生态环境恢复重建工作、制定综合治理策略及区域可持续发展规划提供依据。