小兴安岭黑河胜山林区冬季驼鹿的生境选择

2002年、2003年和2004年的12月至3月,在小兴安岭黑河胜山林场开展了驼鹿生境选择的研究。研究中选择了9类与驼鹿生境选择相关的生态因子：植被型、离公路距离、离采伐点距离、平均雪深、隐蔽程度、坡向、坡位、坡度、海拔,运用SPSS软件进行交叉汇总定量分析。结果表明,胜山驼鹿冬季以落叶阔叶林、灌丛为主要生境,影响驼鹿分布的主要生态因子为隐蔽程度、坡位,其次为雪深、坡向、离采伐点距离、离公路距离,坡度、海拔对驼鹿分布的影响不明显。     

Habitat suitability for tsessebe Damaliscus lunatus lunatus

The distribution and numbers of tsessebe (Damaliscus lunatus lunatus) have declined considerably in South Africa, partly due to deteriorating habitat conditions. Identifying important habitat variables will assist in managing the species. The objective of this study was to identify habitat variables important for tsessebe and to develop a predictive model of habitat selection for this species in a savanna biome. The study was conducted in the Nylsvley Nature Reserve over a 2‐year period. A total of eighteen habitat variables were measured in ten plant communities at 200 sites. Logistic regression analyses were used to identify predictor variables and to construct a habitat model. Tsessebe were found <2 km from the nearest source of water, in flat areas with slopes of <3° and with <10% rockiness. Their distribution was not influenced by the woody component. Sites where tsessebe were present had significantly lower grass heights and tuft heights, with a higher grass density compared with areas not utilized by tsessebe. Nitrogen and sodium levels were also higher at present sites. Habitat type and grass height were the most significant predictors of tsessebe presence. The selected model had an overall percentage prediction of 85.0%. The model was subdivided into five vegetation‐specific models and each model was tested with independent data.     

Scale for resource selection functions

Resource selection functions (RSFs) are statistical models defined to be proportional to the probability of use of a resource unit. My objective with this review is to identify how RSFs can be used to unravel the influence of scale in habitat selection. In wildlife habitat studies, including radiotelemetry, RSFs can be estimated using a variety of statistical methods, all of which can be used to explore the role of scale. All RSFs are bounded by the resolution of data and the spatial extent of the study area, but also allow predictor covariates to be measured at a variety of scales. Conditional logistic regression permits designs (e.g. matched case) that relate the process of habitat selection to a limited domain of resource units that might better characterize what is truly ‘available’ to the animal. Scale influences the process of habitat selection, e.g. food resources are often selected at fine spatial scales, whereas landscape patterns at much larger scales typically influence the location of home ranges. Scale also influences appropriate sampling in many ways: (1) heterogeneity might be obliterated (transmutation) if resolution or grain size is too large, (2) variance of habitat characteristics might be undersampled if extent or domain is too small, (3) timing and duration of observations can influence RSF models, and (d) both spatial and temporal autocorrelations can vary directly with the intensity of sampling. Using RSFs, researchers can examine habitat selection at multiple scales, and predictive models that bridge scales can be estimated. Using Geographical Information Systems, predictor covariates in RSF models can be measured at different scales easily so that the predictive ability of models at alternative spatial and temporal domains can be explored by the investigator. Identification of the scale that best explains the data can be evaluated by comparing alternative models using information‐theoretic metrics such as Akaike Information Criteria, and predictive capability of the models can be assessed using k‐fold cross validation.     

白山原麝国家级自然保护区獐春夏生境选择

生境与动物个体密切相关,生境中元素的不同影响着动物个体对不同生境的选择。2018年5-7月和2019年3-4月在吉林省白山原麝国家级自然保护区对獐（Hydropotes inermis）的春夏季生境选择进行了研究,共记录利用样方104个（春季53个,夏季51个）,对照样方85个（春季46个,夏季39个）。利用卡方检验对植被类型、优势植物、坡位、坡向4种非数值型环境因子进行分析,结果表明春季和夏季獐对这4种环境因子的选择均具有显著性差异,偏好选择以青蒿（Artemisia carvifolia）为优势植物,位于中坡位,坡向为阳坡的草地生境活动。利用独立样本T检验和Mann-Whitney U检验对海拔、人为干扰距离、水源距离、草本覆盖度、优势草本高度、隐蔽级、坡度7种数值型环境因子进行分析,结果表明春季和夏季獐均偏好选择隐蔽级较高（春季30.189±14.609,夏季62.745±29.737）、优势草本高度较高（春季87.359±16.190,84.510±29.618）、坡度较缓的生境（春季14.245±3.721,13.333±5.260）活动。此外,资源选择函数模型对獐的春季和夏季的生境选择预测正确率均大于90%,表明该模型可以较好地预测獐的生境选择。白山原麝国家级自然保护区獐种群数量较小,适宜性栖息地面积较少,应加强对其种群及适宜性生境的保护。     

Temporal,Spatial, and Landscape Habitat Characteristics of Moose—Vehicle Collisions in Western Maine

Abstract: We analyzed moose (Alces alces)-vehicle collisions (MVCs) in western Maine, USA, from 1992 to 2005 (n = 8,156) using Geographic Information Systems to identify patterns of temporal and spatial distribution and develop predictive models based on road and landscape characteristics. We used chi-square and correlation analyses to assess temporal characteristics of MVCs, K-function and kernel analyses to identify spatial clusters of MVCs, and logistic regression to relate covariates for traffic, land-cover, land-form, and relative moose abundance to probability of MVC. We evaluated candidate models using Akaike's Information Criterion, area under the receiver operating characteristic curve (AUC), and the percentage of correctly classified observations. Most (81.6%) MVCs occurred from May to October, with peak monthly frequencies in June (18.6%). Moose-vehicle collisions were clustered spatially on roads at local (0–4 km) and regional scales (22–41 km and 45–54 km), but not at intermediate scales. Traffic-related covariates predicting MVCs included traffic volume and speed limit. For each additional 500 vehicles/day, odds of a location being an MVC increased by 57%. For each 8-km/hr increase in speed limit, odds of an MVC increased by 35%. Landscape composition covariates best predicted MVCs within a 2.5-km radius of the collision site. Mean percent cover within 2.5 km of MVCs was comprised of 36% more cutover forest, 10% more coniferous forest, 5% less deciduous-mixed forest, and 10% less nonwoody wetland than for random points. For every 5% increase in percent cutover and coniferous forest within 2.5 km of the road, predicted odds of MVC increased by 36% and 19%, respectively. Landscape configuration covariates best predicted MVCs within the 5.0-km radius. Moose-vehicle collisions were associated with areas of less interspersion of cover types; for each 5% increase in an index of interspersion-juxtaposition, predicted odds of MVC decreased by 11%. Our final model attained high predictive power (AUC = 0.835) and validation accuracy (75.0%). The model also proved robust to physiographic variation, exhibiting high predictive power (AUC = 0.828) and validation accuracy (68.8%). Managers seeking to prioritize resources for reducing MVCs or predicting future areas of high MVC probability should assess land-cover composition and configuration surrounding MVC hotspots at geographic extents out to 2.5–5 km and use this information to plan expensive roadside management practices such as fencing. The importance of traffic and landscape covariates in our modeling suggests that effective management to reduce MVCs will require a complex combination of driving speed reductions and modifications to forest management along roads.