Multiple spatial-scale resource selection function models in relation to human disturbance for moose in northeastern China

The moose (Alces alces cameloides) population in northeastern China is on the southernmost edge of its distribution in Asia. A survey was conducted to determine moose resource selection and the effects of human disturbance on moose in a study area of 20,661 ha located on the northwestern slope of the Lesser Khingan Mountains, located in northeastern China. Predictive models of resource selection were developed using logistic and autologistic regression. All models considered resource variable selection at two spatial scales, patch and landscape. At the patch scale, moose preferred larger birch (Betula platyphylla) patches, but avoided larger tamarack (Larix gmelinii) patches. At the landscape scale, moose preferred higher densities of tamarack patches, i.e., heterogeneity of tamarack stands, selected areas with more abundant annual shoots, terrain conducive to better concealment, higher altitudes and areas saturated with soil moisture. Roads and forest harvest intervals were identified as important human disturbance factors. This is the first time that moose have been reported to avoid roads, and the avoidance distance was nearly 3 km. We believe that in this region moose under the influence of roads are behaviorally plastic, compared with the indifference of moose to the presence of roads in other regions. Moose avoided forest areas logged more than 3 years previously and preferred areas logged 1–2 years previously. In addition, it may be necessary to monitor the effect of the dynamic of density of roe deer on the spatial distribution of the moose population.     

基于支持向量回归的棉铃虫蛹发育历期估测

温度与发育速率关系模拟是昆虫学研究的一个重要内容, 传统基于经验风险最小的非线性参数模型(Logan模型、Lactin模型和王氏模型)存在诸多弊端。本文基于结构风险最小的改进支持向量回归（SVR）研究温度与棉铃虫Helicoverpa armigera蛹发育历期关系。结果表明: 与传统非线性模型相比, SVR模型性能优异; 基于全部92个样本, SVR模型拟合和留一法预测的决定系数R²分别为0.998和0.996, 估测的蛹期三基点温度更可信。从全部样本中依温度均匀选取部分样本实施独立预测, 当训练集为20个样本时, SVR模型独立预测的R²为0.981, 优于传统非线性模型中独立预测最佳的Lactin模型（R²=0.958）; 当训练集进一步减少到12个样本时, SVR模型的R²仅降低到0.964, 而传统非线性模型均已不适用。结果提示SVR模型在小样本情况下较传统非线性模型优势明显, 在昆虫发育历期估测建模中有应用前景。     

Developing landscape habitat models for rare amphibians with small geographic ranges: a case study of Siskiyou Mountains salamanders in the western USA

To advance the development of conservation planning for rare species with small geographic ranges, we determined habitat associations of Siskiyou Mountains salamanders (Plethodon stormi) and developed habitat suitability models at fine (10 ha), medium (40 ha), and broad (202 ha) spatial scales using available Geographic Information Systems data and logistic regression analysis with an information theoretic approach. Across spatial scales, there was very little support for models with structural habitat features, such as tree canopy cover and conifer diameter. Model-averaged 95% confidence intervals for regression coefficients and associated odds ratios indicated that the occurrence of Siskiyou Mountains salamanders was positively associated with rocky soils and Pacific madrone (Abutus menziesii) and negatively associated with elevation and white fir (Abies concolor); these associations were consistent across 3 spatial scales. The occurrence of this species also was positively associated with hardwood density at the medium spatial scale. Odds ratios projected that a 10% decrease in white fir abundance would increase the odds of salamander occurrence 3.02–4.47 times, depending on spatial scale. We selected the model with rocky soils, white fir, and Oregon white oak (Quercus garryana) as the best model across 3 spatial scales and created habitat suitability maps for Siskiyou Mountains salamanders by projecting habitat suitability scores across the landscape. Our habitat suitability models and maps are applicable to selection of priority conservation areas for Siskiyou Mountains salamanders, and our approach can be easily adapted to conservation of other rare species in any geographical location.     

Variable selection for logistic regression using a prediction-focused information criterion

In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the focused information criterion (FIC) for variable selection in logistic regression. The FIC gives, depending on the quantity to be estimated, possibly different sets of selected variables. The standard version of the FIC measures the mean squared error of the estimator of the quantity of interest in the selected model. In this article, we propose more general versions of the FIC, allowing other risk measures such as the one based on L(p) error. When prediction of an event is important, as is often the case in medical applications, we construct an FIC using the error rate as a natural risk measure. The advantages of using an information criterion which depends on both the quantity of interest and the selected risk measure are illustrated by means of a simulation study and application to a study on diabetic retinopathy.     

昆虫发育进度的非正态描述──分段指数曲线

提出了分段连续指数曲线转化为多元线性回归方程的方法。利用取剩余平方和最小的方法确定未知折点。给出了在描述昆虫发育进度的模拟模型。     

Predicting patch occupancy in fragmented landscapes at the rangewide scale for an endangered species: an example of an American warbler

AIM: Our objective was to identify the distribution of the endangered golden-cheeked warbler (Setophaga chrysoparia) in fragmented oak-juniper woodlands by applying a geoadditive semiparametric occupancy model to better assist decision-makers in identifying suitable habitat across the species breeding range on which conservation or mitigation activities can be focused and thus prioritize management and conservation planning. LOCATION: Texas, USA. METHODS: We used repeated double-observer detection/non-detection surveys of randomly selected (n = 287) patches of potential habitat to evaluate warbler patch-scale presence across the species breeding range. We used a geoadditive semiparametric occupancy model with remotely sensed habitat metrics (patch size and landscape composition) to predict patch-scale occupancy of golden-cheeked warblers in the fragmented oak-juniper woodlands of central Texas, USA. RESULTS: Our spatially explicit model indicated that golden-cheeked warbler patch occupancy declined from south to north within the breeding range concomitant with reductions in the availability of large habitat patches. We found that 59% of woodland patches, primarily in the northern and central portions of the warbler's range, were predicted to have occupancy probabilities ≤0.10 with only 3% of patches predicted to have occupancy probabilities >0.90. Our model exhibited high prediction accuracy (area under curve = 0.91) when validated using independently collected warbler occurrence data. MAIN CONCLUSIONS: We have identified a distinct spatial occurrence gradient for golden-cheeked warblers as well as a relationship between two measurable landscape characteristics. Because habitat-occupancy relationships were key drivers of our model, our results can be used to identify potential areas where conservation actions supporting habitat mitigation can occur and identify areas where conservation of future potential habitat is possible. Additionally, our results can be used to focus resources on maintenance and creation of patches that are more likely to harbour viable local warbler populations.     

Carbon pool densities and a first estimate of the total carbon pool in the Mongolian forest‐steppe

The boreal forest biome represents one of the most important terrestrial carbon stores, which gave reason to intensive research on carbon stock densities. However, such an analysis does not yet exist for the southernmost Eurosiberian boreal forests in Inner Asia. Most of these forests are located in the Mongolian forest‐steppe, which is largely dominated by Larix sibirica. We quantified the carbon stock density and total carbon pool of Mongolia's boreal forests and adjacent grasslands and draw conclusions on possible future change. Mean aboveground carbon stock density in the interior of L. sibirica forests was 66 Mg C ha^?1, which is in the upper range of values reported from boreal forests and probably due to the comparably long growing season. The density of soil organic carbon (SOC, 108 Mg C ha^?1) and total belowground carbon density (149 Mg C ha^?1) are at the lower end of the range known from boreal forests, which might be the result of higher soil temperatures and a thinner permafrost layer than in the central and northern boreal forest belt. Land use effects are especially relevant at forest edges, where mean carbon stock density was 188 Mg C ha^?1, compared with 215 Mg C ha^?1 in the forest interior. Carbon stock density in grasslands was 144 Mg C ha^?1. Analysis of satellite imagery of the highly fragmented forest area in the forest‐steppe zone showed that Mongolia's total boreal forest area is currently 73 818 km², and 22% of this area refers to forest edges (defined as the first 30 m from the edge). The total forest carbon pool of Mongolia was estimated at ~ 1.5?1.7 Pg C, a value which is likely to decrease in future with increasing deforestation and fire frequency, and global warming.     

以小时为步长的大兴安岭兴安落叶松林地表可燃物含水率预测模型

应用时滞平衡含水率法(包括Nelson和Simard两种)及气象要素回归法,以小时为步长对大兴安岭地区阳坡上部落叶松林下地表细小死可燃物进行动态含水率测定,分析了不同郁闭度林分的预测误差.结果表明:以小时为步长的可燃物含水率预测方法适用于大兴安岭地区的典型落叶松林分,Simard法预测平均绝对误差为1.1%,平均相对误差为8.5%,低于Nelson法和传统的气象要素回归法,接近同类研究的误差范围;同一坡度、坡位上,不同郁闭度下的可燃物含水率变化差异较大,使用以小时为步长的可燃物含水率预测方法,应根据不同地区林分和地点选择合适的平衡含水率模型,或建立基于林分的可燃物含水率模型.     

Multikink quantile regression for longitudinal data with application to progesterone data analysis

Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multikink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. In this paper, we first propose a multikink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within-subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two proposed estimators are established. Second, we construct a rank score test based on partial subgradients for the existence of the kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in 5 to 6 days after ovulation and then changes to stable again or drops slightly.