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.     

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.     

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.     

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。     

井冈山森林碳储量与LAI和材积的相关性

建立林木生物量模型是估算森林生物量的重要方法之一,叶面积指数（Leaf Area Index,简称LAI）和材积与林木密切相关,是否可通过建立森林生物量与LAI或材积的相关模型来估算森林生物量,进而估算森林碳储量,值得探索。以井冈山自然保护区两种典型森林类型（常绿阔叶林和人工杉木林）为研究对象,分乔木层、植被层和总体（植被层＋土壤层）3部分分别计算碳密度,并对它们与叶面积指数LAI和材积之间的相关性进行分析。结果表明：常绿阔叶林总体碳密度为38．915kg／m^2,高于人工杉木林的27．460kg／m^2;两种森林类型乔木层和植被层碳密度与材积具有很好的相关性（R^2〉0．97）,在与LAI的相关性分析中,人工杉木林乔木层和植被层碳密度与LAI相关系数达到0．7以上,相关关系显著,而常绿阔叶林各层碳密度与LAI的相关性不明显;在森林总体碳密度与LAI和材积的相关性分析中发现,只有常绿阔叶林总体碳密度与材积的R^2为0．7116,达到显著水平,其它相关性水平均不显著。因此,利用材积与生物量和碳储量的相关关系来推算井冈山森林生物量和碳储量的方法是可行的,通过叶面积指数来推算森林生物量和碳储量的方法还有待进一步研究探讨。     

近50年三江源地表湿润指数变化特征及其影响因素

基于三江源区1959—2008年月平均气温、最高气温、最低气温、相对湿度、降水量、风速和日照百分率等气候要素资料,应用修订的Penman-Monteith（P-M）模型计算了最大潜在蒸散量和地表湿润指数,分析其空间分布、年际和年代际变化特征及其主要气象因子的影响。结果表明：1959—2008年间,研究区年降水量呈增加趋势,降水量变化曲线线性拟合倾向率为5.316~13.047 mm.（10a）-1,春夏季增幅较大;最大潜在蒸散量呈增加趋势,年最大潜在蒸散量变化曲线线性拟合倾向率在5.073~10.712 mm.（10a）-1,夏季增幅最大;地表湿润指数变化也呈增加趋势,年地表湿润指数变化曲线线性拟合倾向率0.011~0.026（10a）-1,冬季增幅最大,在15年周期附近,出现了3~5个干湿交替期,1984年之后为偏湿期,在中高频区,1998—2006年有偏干振荡;影响三江源区地表湿润指数的主要因子是降水量、相对湿度和平均最高气温。     

基于指标规范变换的生态环境质量评价普适指数公式

为了建立科学合理、计算简便和实用性强的生态环境质量评价普适指数公式,在确定典型区域的生态环境评价指标体系及其分级标准的基础上,适当设定各项生态环境指标的参照值和指标值的(规范)变换式,使不同指标同级标准值的变换值差异较小,而变换值的对数值差异更小,从而可以认为用变换值表示的不同指标皆等效于某个指标。因此,用变换值表示的不同指标的某种类型的生态环境质量评价指数公式都可以用该等效指标的生态环境质量评价指数公式代替。在满足一定优化目标准则的条件下,用免疫进化粒子群混洗蛙跳算法对公式中的普适参数进行优化,分别得到优化后对23项生态环境指标皆适用的对数型幂函数、韦伯-费希纳定律、Γ型分布函数、污染危害S型函数和加权加和型幂函数5种形式的生态环质量评价普适指数公式,并对公式的可靠性进行了理论分析,对其实用效果进行了检验。     

黄土丘陵沟壑区典型植物耐旱生理及抗旱性评价

通过实地采样获取黄土高原丘陵区柠条、侧柏、油松、山杏和山毛桃5种常见植物类型的4个耐旱指标(丙二醛含量、叶绿素含量、谷胱甘肽含量、束缚水/自由水含量比值)和2个抗旱指标(脯氨酸含量、可溶性糖含量)数据,采用模糊数学隶属函数法和灰色关联分析法评价不同植物的抗旱能力和不同指标的指示效果.结果表明,不同树种的上述6个生理指标对干旱条件的响应存在显著差异,但基于单个指标获得的树种抗旱能力排序结果不完全一致,说明单个指标无法全面描述树种的抗旱能力,对5个树种抗旱能力大小的综合评价结果为柠条＞侧柏＞油松＞山杏＞山毛桃.耐旱指标叶绿素含量、束缚水/自由水含量比值、谷胱甘肽含量和抗旱指标脯氨酸含量与抗旱隶属度的关联度值均＞0.60,可用以指示植物的抗旱能力,其中,以脯氨酸含量的指示效果最好.     

旅游公路景观协调性评价指标权重模型研究

为评价旅游公路景观协调性,通过对影响旅游公路景观协调性因素的分析,采用目标分解法初选评价指标,确定公路景观与交通安全、景观与视觉、景观与生态环境协调性3个目标层。在此基础上采用德尔菲法建立景观协调性评价指标体系;将景观协调性指标权重分为价值量权重和信息量权重2部分,在序关系分析法基础上充分利用指标值信息,综合主、客观赋权法,建立旅游公路景观协调性评价指标组合权重模型。结果表明:该模型可客观有效地确定各评价指标的权重。     

基于本质安全的供应链风险识别方法研究

由于集成供应链的构成较为复杂,来自供应链内部、外部的风险因素众多,单凭建立附加的防护系统并不能从根本上解决集成供应链的风险损失问题。因而需要从本质安全的视角出发,从根源上辨识风险源,及时发现供应链中的薄弱环节,消除因风险隐患及控制风险而造成的损失。SCOR模型可以清晰地将整个供应链的组成结构、操作流程描述出来,以此为基础运用模型的顶层、配置层、元素层和实施层这4个层级对集成供应链风险进行了研究。根据集成供应链主体构成的特点将供应链整体风险划分为5类,分别为需求风险、供应风险、运作过程风险、环境风险以及制度风险。并通过建立供应链风险识别指标体系以有针对性地识别某一企业的供应链风险。