高黎贡山雉类生境预测

物种分布信息对野生动物的保护和管理至关重要。基于物种访查数据和气候数据,采用基于物种生境偏好、利比希最小因子定律和谢尔福德耐受性定律构建的生态位模型,综合考虑物种出现点和环境变量,预测了高黎贡山白尾梢虹雉Lophophorus sclateri、血雉Ithaginis cruentus、白鹇Lophura nycthemera、白腹锦鸡Chrysolophus amherstiae的潜在分布区域。结果表明,模型对4种雉类的预测均达到较好效果;白尾梢虹雉、血雉、白鹇和白腹锦鸡的潜在生境总面积分别为6 432 km^2、8 464 km^2、9 573 km^2和13 691 km^2,白鹇和白腹锦鸡的潜在生境面积大于白尾梢虹雉和血雉,但后两者具有更多的高质量生境。高黎贡山北段是4种雉类潜在生境的重叠区,为高黎贡山雉类保护的优先区域,尤其是白尾梢虹雉,建议加大该区域的雉类调查和保护力度。     

2型糖尿病模型小鼠造模时间的选择

目的:分析不同时期的2型糖尿病小鼠血生化指标及心肌和肾脏的病理变化情况,为选择2型糖尿病模型小鼠造模时间提供依据。方法:32只健康雄性ICR小鼠高脂饲料喂养6周后,腹腔注射链脲佐菌素(STZ,30mg/kg),连续5d,制备糖尿病模型。9d后测空腹血糖(FBG),高于11.1mmol/L视为糖尿病模型。分别于成模后第4、6、8周处死一组小鼠。另取8只雄性ICR小鼠作为对照组,常规饲料喂养,于糖尿病组小鼠成模后第8周处死。分析小鼠生化及病理情况:①心脏、肾脏脏器系数计算;②血清乳酸脱氢酶(LDH)、肌酸激酶(CK)、肌酐(Cr)和尿素氮(BUN)含量测定;③HE染色观察心肌和肾脏组织病变的整体情况;Masson染色观察心肌组织纤维化情况;PAS染色观察肾脏组织病理变化。结果:与对照组小鼠进行比较,第4、6、8周的糖尿病小鼠心脏器系数升高,血清LDH、CK升高,病理组织学见心肌细胞肥大,纤维化;肾脏脏器系数升高,肾功能肌酐(Cr)、尿素氮(BUN)显著升高,病理组织学见肾小球肥大,肾小管基底膜增厚,管腔萎缩。结论:第6周糖尿病小鼠相关生化病理指标改变相对明显且饲养时间相对较短,故2型糖尿病模型小鼠造模后第6周是进行药物干预和病理、生理、生化等研究的最佳时间。     

Physiological model using diffuse reflectance spectroscopy for nonmelanoma skin cancer diagnosis

Diffuse reflectance spectroscopy (DRS) is a noninvasive, fast, and low‐cost technology with potential to assist cancer diagnosis. The goal of this study was to test the capability of our physiological model, a computational Monte Carlo lookup table inverse model, for nonmelanoma skin cancer diagnosis. We applied this model on a clinical DRS dataset to extract scattering parameters, blood volume fraction, oxygen saturation and vessel radius. We found that the model was able to capture physiological information relevant to skin cancer. We used the extracted parameters to classify (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]) vs actinic keratosis (AK) and (BCC, SCC, AK) vs normal. The area under the receiver operating characteristic curve achieved by the classifiers trained on the parameters extracted using the physiological model is comparable to that of classifiers trained on features extracted via Principal Component Analysis. Our findings suggest that DRS can reveal physiologic characteristics of skin and this physiologic model offers greater flexibility for diagnosing skin cancer than a pure statistical analysis. Physiological parameters extracted from diffuse reflectance spectra data for nonmelanoma skin cancer diagnosis.     

Construction of a Model Culture System of Human Colonic Microbiota to Detect Decreased Lachnospiraceae Abundance and Butyrogenesis in the Feces of Ulcerative Colitis Patients

Compositional alteration of the gut microbiota is associated with ulcerative colitis (UC). Here, a model culture system is established for the in vitro human colonic microbiota of UC, which will be helpful for determining medical interventions. 16S ribosomal RNA sequencing confirms that UC models are successfully developed from fecal inoculum and retain the bacterial species biodiversity of UC feces. The UC models closely reproduce the microbial components and successfully preserve distinct clusters from the healthy subjects (HS), as observed in the feces. The relative abundance of bacteria belonging to the family Lachnospiraceae significantly decreases in the UC models compared to that in HS, as observed in the feces. The system detects significantly lower butyrogenesis in the UC models than that in HS, correlating with the decreased abundance of Lachnospiraceae. Interestingly, the relative abundance of Lachnospiraceae does not correlate with disease activity (defined as partial Mayo score), suggesting that Lachnospiraceae persists in UC patients at a decreased level, irrespective of the alteration in disease activity. Moreover, the system shows that administration of Clostridium butyricum MIYAIRI restores butyrogenesis in the UC model. Hence, the model detects deregulation in the intestinal environment in UC patients and may be useful for simulating the effect of probiotics.     

Parasitism and alternative reproductive tactics in Northern chamois

Alternative reproductive tactics (ARTs), discrete phenotypic variations evolved to maximize fitness, may entail different cost‐benefit trade‐offs. In large mammals, differences in costs associated with ARTs—including energy expenditure and parasite infection—are typically greatest during the breeding season. Nonetheless, physiological and behavioral differences between ARTs can manifest throughout the year, possibly involving costs that may contribute to maintain ARTs within populations. Using the number of nematode larvae per gram of feces (LPG) as a proxy, we explored the temporal changes in lung parasite infection in territorial and nonterritorial male chamois Rupicapra in the Gran Paradiso National Park (Italy), between 2011 and 2012. We aimed to identify which tactic‐specific physiological and behavioral features (including age, hormonal levels, inter‐ and intrasexual interactions, and space use) or climatic factors (temperature and precipitation) best explained yearly variation in parasite infection within and between ARTs. Generalized additive mixed models showed that the fecal larval counts of lung nematodes underwent strong temporal changes in both male types. Differences between ARTs (with higher LPG values in territorial than nonterritorial males) were greatest during the rut and—to a lesser extent—in spring, respectively, at the peak and at the onset of territoriality. The difference in LPG between tactics was largely explained by the greater levels of hormone metabolites in territorial males during the rut. The other variables did not contribute significantly to explain the different shedding of larvae within and between ARTs. Our analysis suggests that different values of LPG between territorial and nonterritorial males are largely a result of tactic‐specific differences in the secretion of hormone metabolites, but only during the rut. To clarify whether rut‐related parasitism contributes to the maintenance of ARTs, tactic‐specific life history trade‐offs, for example, between reproduction and parasite‐related mortality, must be investigated.     

Data partitioning and correction for ascertainment bias reduce the uncertainty of placental mammal divergence times inferred from the morphological clock

Bayesian estimates of divergence times based on the molecular clock yield uncertainty of parameter estimates measured by the width of posterior distributions of node ages. For the relaxed molecular clock, previous works have reported that some of the uncertainty inherent to the variation of rates among lineages may be reduced by partitioning data. Here we test this effect for the purely morphological clock, using placental mammals as a case study. We applied the uncorrelated lognormal relaxed clock to morphological data of 40 extant mammalian taxa and 4,533 characters, taken from the largest published matrix of discrete phenotypic characters. The morphologically derived timescale was compared to divergence times inferred from molecular and combined data. We show that partitioning data into anatomical units significantly reduced the uncertainty of divergence time estimates for morphological data. For the first time, we demonstrate that ascertainment bias has an impact on the precision of morphological clock estimates. While analyses including molecular data suggested most divergences between placental orders occurred near the K‐Pg boundary, the partitioned morphological clock recovered older interordinal splits and some younger intraordinal ones, including significantly later dates for the radiation of bats and rodents, which accord to the short‐fuse hypothesis.     

Influence of climate change and postdelisting management on long‐term population viability of the conservation‐reliant Kirtland's Warbler

Rapid global climate change is resulting in novel abiotic and biotic conditions and interactions. Identifying management strategies that maximize probability of long‐term persistence requires an understanding of the vulnerability of species to environmental changes. We sought to quantify the vulnerability of Kirtland's Warbler (Setophaga kirtlandii), a rare Neotropical migratory songbird that breeds almost exclusively in the Lower Peninsula of Michigan and winters in the Bahamian Archipelago, to projected environmental changes on the breeding and wintering grounds. We developed a population‐level simulation model that incorporates the influence of annual environmental conditions on the breeding and wintering grounds, and parameterized the model using empirical relationships. We simulated independent and additive effects of reduced breeding grounds habitat quantity and quality, and wintering grounds habitat quality, on population viability. Our results indicated the Kirtland's Warbler population is stable under current environmental and management conditions. Reduced breeding grounds habitat quantity resulted in reductions of the stable population size, but did not cause extinction under the scenarios we examined. In contrast, projected large reductions in wintering grounds precipitation caused the population to decline, with risk of extinction magnified when breeding habitat quantity or quality also decreased. Our study indicates that probability of long‐term persistence for Kirtland's Warbler will depend on climate change impacts to wintering grounds habitat quality and contributes to the growing literature documenting the importance of considering the full annual cycle for understanding population dynamics of migratory species.     

Estimability of migration survival rates from integrated breeding and winter capture–recapture data

Long‐distance migration is a common phenomenon across the animal kingdom but the scale of annual migratory movements has made it difficult for researchers to estimate survival rates during these periods of the annual cycle. Estimating migration survival is particularly challenging for small‐bodied species that cannot carry satellite tags, a group that includes the vast majority of migratory species. When capture–recapture data are available for linked breeding and non‐breeding populations, estimation of overall migration survival is possible but current methods do not allow separate estimation of spring and autumn survival rates. Recent development of a Bayesian integrated survival model has provided a method to separately estimate the latent spring and autumn survival rates using capture–recapture data, though the accuracy and precision of these estimates has not been formally tested. Here, I used simulated data to explore the estimability of migration survival rates using this model. Under a variety of biologically realistic scenarios, I demonstrate that spring and autumn migration survival can be estimated from the integrated survival model, though estimates are biased toward the overall migration survival probability. The direction and magnitude of this bias are influenced by the relative difference in spring and autumn survival rates as well as the degree of annual variation in these rates. The inclusion of covariates can improve the model's performance, especially when annual variation in migration survival rates is low. Migration survival rates can be estimated from relatively short time series (4–5 years), but bias and precision of estimates are improved when longer time series (10–12 years) are available. The ability to estimate seasonal survival rates of small, migratory organisms opens the door to advancing our understanding of the ecology and conservation of these species. Application of this method will enable researchers to better understand when mortality occurs across the annual cycle and how the migratory periods contribute to population dynamics. Integrating summer and winter capture data requires knowledge of the migratory connectivity of sampled populations and therefore efforts to simultaneously collect both survival and tracking data should be a high priority, especially for species of conservation concern.