人工林赤腹松鼠危害的空间分布格局

2009年8月和12月,采用样方调查法,对四川省洪雅县柳杉(Crypotomeria fortunei)和杉木(Cunninghami lanceolata)人工混交林的赤腹松鼠(Callosciurus erythraeus)危害进行了调查和分析.共调查了142个10 m×10 m的样方,采用坐标记录法,记录样方内每株林木的坐标值、树种、胸径、冠幅、株下草本盖度、树冠交叠度、是否邻近林窗及其它非生物生境因子(小路、公路、溪水、悬崖)、受害(2 mo以内,以痕迹开始发黑区分)程度共9个参数,共收集3 560株林木数据.分别采用方差均值法和最近邻体法对受害株在研究区和样方尺度上的空间格局进行分析,并在样方尺度上探究植株受害程度与生境因子的关系.结果显示,在研究区尺度上赤腹松鼠危害呈聚集分布,在样方尺度上则以随机分布为主.植株受害程度与其株下草本盖度呈正相关(P=0.007),与树冠交叠度也呈现出正相关(P=0.029),而与其它因子没有显著性的相关性(P>0.05).因此认为赤腹松鼠对人工林林木的危害在研究区尺度上是与其生活环境需求相关的,如食物资源、隐蔽条件等;在对林木株的危害选择上则是随机的,但林木株受害的程度与其株下的草本盖度和树冠的交叠度呈现出显著的正相关关系.图2表5参30     

华山松松茎象幼虫的危害及其防治研究

华山松松茎象是南岳景区松树上新发现的一种重要害虫,该虫严重危害华山松和黑松.本文介绍了其幼虫的取食危害特点及防治方法.该虫幼虫取食华山松和黑松的韧皮部,同时取食新鲜的松脂,越冬后的幼虫仅靠取食新鲜松脂就能正常发育,并化蛹、羽化.单株虫口数量与松树树干基部直径呈明显正相关,即树干基部直径在10.0~20.0 cm 、20.1~25.0 cm 、25.1~35.0 cm之间的松树,平均单株有虫数量分别为1.0头株-1、1.98头株-1和3.30头.株-1.在5~10月间,采用人工清除幼虫的方法可有效地控制该虫的危害,采用化学药剂防治该虫效果不明显.     

衡阳地区樗蚕的发生与防治技术研究

樗蚕(Philosamia cyntia Walker et Felder)是城镇园林植物上的重要害虫.本文研究了该虫在湖南省衡阳地区的生物学特性、发生规律与防治技术.结果表明,该虫在衡阳地区1年发生3代,以蛹在寄主植物上的丝质茧内越冬.5月中下旬是第1代幼虫危害高峰期,也是全年危害最严重的时期,必须采用适当的方法进行防治.在低虫口密度时,采用剪除越冬虫茧的方法,降低越冬代虫口基数.在高虫口密度时,使用高效、低毒、低残留的杀虫剂喷雾防治幼虫,可以迅速压低虫口密度.对于高大树上的樗蚕,可在其幼虫取食期,采用树干打孔注入内吸性杀虫剂的方式,有很好的防治效果.采用克百威、铁灭克等内吸、传导作用强的杀虫剂埋根的方法防治樗蚕幼虫,防效好,药效期长.     

林窗大小和位置对丽江云杉自然更新幼苗存活和生长的影响

通过 1997～ 2 0 0 0年对 32个林窗的连续观测调查 ,研究了滇西北玉龙雪山自然保护区云杉坪亚高山丽江云杉林林窗大小和林窗位置对自然更新幼苗存活和生长的影响 .4个生长季节的观测结果表明 :林窗与林下非林窗内的幼苗大小和幼苗存活数量差异明显 .林窗由小到大 ,单位面积内的自然更新苗木数量逐渐减少 ,小林窗中更新苗为大林窗的 5倍左右 ,而林下的更新苗很少 ,0 .2ind .10m-2 .中等林窗和小林窗内的幼苗数量在从南到中心到北的位置上几乎没有明显的差异 ;大林窗中存在由南到北的位置差异 ,更新幼苗数量逐渐减少 .从更新幼苗的生长来看 ,中等林窗内的幼苗 ,高度最大、生长最快 ,定居阶段的平均年高生长为 (7.1± 0 .5 )cma-1,小林窗次之 ,大林窗和林下幼苗个体最小 ,生长最慢 .进一步从林窗位置来看 ,中、小林窗幼苗大小几无位置差异 ,大林窗则由南到北 ,幼苗由大变小 .从幼苗存活数量和大小来看 ,中等林窗大小是丽江云杉幼苗更新的适宜面积 ,对该类型退化亚高山针叶林恢复提供了一定的参考 .图 4表 2参 2 3     

迁徙停歇地东方白鹳繁殖生态研究

黄河三角洲是东方白鹳的迁徙停歇地之一,但近些年开始出现繁殖种群.为了解该种群的繁殖现状,有效开展保护工作,于2009年在黄河三角洲对东方白鹳的繁殖生态进行了研究.2009年在黄河三角洲繁殖的东方白鹳种群数量为21对,繁殖个体于2月上旬陆续返回繁殖区,最早于2月21开始筑巢.巢筑于水泥电线杆、人工招引巢或者高压输电铁塔上,其中利用旧巢11巢,新建巢10巢.大汶流巢区平均巢高13.25 m±2.07 m(N=18)、巢间距647.22 m±1 086.49 m(N=18);黄河口巢区平均巢高25.50 m±7.97 m(N=3),巢间距42 640.00 m±62 838.80 m(N=3).孵化期最早始于2月25日,个别繁殖对受干扰影响延迟到5月中旬.孵化期32.07 d±1.34 d(N=15),育雏期63.33 d±6.83 d(N=12),日育雏6.23±2.23次(N=68),雏鸟最早离巢时间为5月28日,最晚离巢时间为8月19日.2009年的21对繁殖东方白鹳共有17对繁殖成功,孵出47只幼鸟,出飞幼鸟37只.影响东方白鹳繁殖的主要因素是强风,此外,游客干扰、适宜巢址缺乏也是影响繁殖的重要因素.为提高人工招引繁殖的成功率,可适当增加人工招引巢数量并对其上的巢基进行加固.     

温度和降雨对长白山阔叶红松林蝶类组成和多样性的影响(英文)

于2005～2007年采用样带调查法在长白山阔叶红松林带对蝶类的物种组成和个体数量进行了调查,通过逐步回归方程分析了蝶类组成和多样性与各气候因子之间的关系,重点分析了温度和降水对蝶类组成及多样性的影响.共采集蝶类标本768个,隶属于7科69种.通过分析可知,不同年份和不同月份之间,蝶类个体数量和多样性无明显差异;逐步回归结果显示,月平均温度是影响该区域蝶类组成种类和多样性的重要因子,而月降雨量是影响蝶类个体数量的重要气候因子.蝶类各科组成与气候因子之间的关系不同,前3个月累积温度和降雨量对蝶类影响范围较大,如粉蝶科、蛱蝶科、眼蝶科.不同优势种与气候因子之间的相关性也有差异.研究为区域生物多样性保护政策的制定提供了理论依据.     

长白山阔叶红松林不同演替阶段地表甲虫组成和多样性(英文)

鞘翅目昆虫多样性的变化是森林演替过程的综合反映.于2007年6~8月采用陷阱诱捕法对长白山阔叶红松林带不同演替阶段地表甲虫物种组成和数量进行了调查,并分析了该地区不同演替阶段地表甲虫多样性的变化趋势及主要生境因子对地表甲虫群落的影响.结果显示,长白山阔叶红松林内共诱捕地表甲虫23种,共511头,隶属于10个科.其中个体数最多的为埋葬甲科,物种数最多的为步甲科,优势类群为步甲科和埋葬甲科.不同演替阶段中,次生白桦林地表甲虫物种数和个体数高于原始阔叶红松林和次生针阔混交林,3个生境内地表甲虫多样性无显著差别.地表甲虫高峰期为7月份.不同演替阶段的样地中物种统计数量都没有达到渐进线,次生白桦林样地中实际物种只占估计值的67%,其它2个生境实际物种数都在物种估计值的95%区间范围内,略低于平均值.3个生境的地表甲虫种-多度曲线无显著差异,符合对数分布.胸高断面积和土壤湿度对地表甲虫的分布有显著影响,它们可以解释99.2%的物种与环境之间的关系.     

不同比例钙锌共存对土壤镉有效性的影响及其机制

采用盆栽试验,研究了赤红壤上两种镉污染水平下,施用不同比例钙锌对小油菜(Brassica Campetris,Lvar Conmuni)生物量、镉吸收量及土壤溶液中镉、钙、锌质量浓度的影响.结果表明,钙、锌以不同比例共存时并不会对赤红壤上小油菜的生长产生明显的影响;随着锌用量增加,土壤溶液中锌的质量浓度明显增加,土壤溶液中镉的质量浓度明显升高.小油菜体内镉含量明显降低;高镉污染赤红壤上,钙锌共存中钙用量增加,土壤溶液中钙的质量浓度明显增加.低、高镉污染赤红壤上,钙、锌共存摩尔比例为4:1时,小油菜体内镉含量较对照平均分别降低34.2%和27.3%(两季平均值);而土壤溶液中镉的质量浓度较对照分别增加307%和120%.在低镉污染赤红壤上,锌施用量与小油菜体内镉含量呈显著负相关;高镉污染赤红壤上,锌施用量与土壤溶液中镉的质量浓度呈显著正相关.钙、锌以不同比例施入土壤时,锌施用量多少是控制土壤镉有效性高低的主要因素.     

生物学方法在环境内分泌干扰物检测及评价中的应用

环境内分泌干扰物(EEDs)不仅对人类和动物本身机体具有潜在危害,还可通过生殖系统影响后代.因此,对EEDs的检测与评价非常重要.本文主要从个体、组织、细胞和分子水平对筛选与评价EEDs的生物学方法进行综述.     

长白山阔叶红松林林分空间结构

在长白山北坡自然保护区内,建立面积为1hm~2的阔叶红松林永久标准样地,对胸径1cm以上的每株树木进行定位监测,用空间结构参数——混交度、大小比数和角尺度分析该林分的空间结构.结果表明:该林分树木个体总体上处于中度混交水平,处于乔木上层和中层的树种混交度以强度混交和完全混交为主,明显高于乔木下层树种的混交度;基于胸径计算的大小比数和基于树高计算的大小比数的结果基本一致,都能客观反映该林分在垂直方向上的分化;树木个体整体分布格局与树木大小密切相关,当树木个体起测径阶1cm≤DBH6cm时,林分呈聚集分布的格局,当起测径阶DBH6cm时,林分呈随机分布的格局.图1表4参25     

Estimating Population Size with Noninvasive Capture-Mark-Recapture Data

Abstract:  Estimating population size of elusive and rare species is challenging. The difficulties in catching such species has triggered the use of samples collected noninvasively, such as feces or hair, from which genetic analysis yields data similar to capture-mark-recapture (CMR) data. There are, however, two differences between classical CMR and noninvasive CMR. First, capture and recapture data are gathered over multiple sampling sessions in classical CMR, whereas in noninvasive CMR they can be obtained from a single sampling session. Second, because of genotyping errors and unlike classical CMR, there is no simple relationship between (genetic) marks and individuals in noninvasive CMR. We evaluated, through simulations, the reliability of population size estimates based on noninvasive CMR. For equal sampling efforts, we compared estimates of population size N obtained from accumulation curves, a maximum likelihood, and a Bayesian estimator. For a closed population and without sampling heterogeneity, estimates obtained from noninvasive CMR were as reliable as estimates from classical CMR. The sampling structure (single or multiple session) did not alter the results, the Bayesian estimator in the case of a single sampling session presented the best compromise between low mean squared error and a 95% confidence interval encompassing the parametric value of N in most simulations. Finally, when suitable field and lab protocols were used, genotyping errors did not substantially bias population size estimates (bias < 3.5% in all simulations). The ability to reliably estimate population size from noninvasive samples taken during a single session offers a new and useful technique for the management and conservation of elusive and rare species.     

Estimating the population size with a behavioral response in capture-recapture experiment

A new estimating procedure is suggested to estimate the population size in a capture-recapture experiment. The capture intensities for first-capture and recapture are allowed to be different and time dependent but they are assumed to be proportional. It is shown that the information on the proportionality constant is crucial to the estimation of the population size. Sensitivity analysis with a misspecification of the proportionality constant is conducted. The method has also been extended to the case with an unknown proportionality. A real example is given.     

Effective Population Size in Red-Cockaded Woodpeckers: Population and Model Differences

Loss of genetic variability in isolated populations is an important issue for conservation biology. Most studies involve only a single population of a given species and a single method of estimating rate of loss. Here we present analyses for three different Red-cockaded Woodpecker ( Picoides borealis ) populations from different geographic regions. We compare two different models for estimating the expected rate of loss of genetic variability, and test their sensitivity to model parameters. We found that the simpler model (Reed et al. 1988) consistently estimated a greater rate of loss of genetic variability from a population than did the Emigh and Pollak (1979) model. The ratio of effective population size (which describes the expected rate of loss of genetic variability) to breeder population size varied widely among Red-cockaded Woodpecker populations due to geographic variation in demography. For this species, estimates of effective size were extremely sensitive to survival parameters, but not to the probability of breeding or reproductive success. Sensitivity was sufficient that error in estimating survival rates in the field could easily mask true population differences in effective size. Our results indicate that accurate and precise demographic data are prerequisites to determining effective population size for this species using genetic models, and that a single estimate of rate of loss of genetic variability is not valid across populations.     

Hierarchical spatial modeling for estimation of population size

Estimation of population size has traditionally been viewed from a finite population sampling perspective. Typically, the objective is to obtain an estimate of the total population count of individuals within some region. Often, some stratification scheme is used to estimate counts on subregions, whereby the total count is obtained by aggregation with weights, say, proportional to the areas of the subregions. We offer an alternative to the finite population sampling approach for estimating population size. The method does not require that the subregions on which counts are available form a complete partition of the region of interest. In fact, we envision counts coming from areal units that are small relative to the entire study region and that the total area sampled is a very small proportion of the total study area. In extrapolating to the entire region, we might benefit from assuming that there is spatial structure to the counts. We implement this by modeling the intensity surface as a realization from a spatially correlated random process. In the case of multiple population or species counts, we use the linear model of coregionalization to specify a multivariate process which provides associated intensity surfaces hence association between counts within and across areal units. We illustrate the method of population size estimation with simulated data and with tree counts from a Southwestern pinyon-juniper woodland data set.     

Effective Population Size in Winter-Run Chinook Salmon

Winter-run chinook salmon from the Sacramento River, California, is federally listed as endangered. Since 1989 there has been aprogram to augment the natural population by capturing adults, artificially spawning them, raising tine young and releasing the smolt. Here we estimate the effective population size of these captive-raised fish, the natural run, and the combination of both groups over the three-year period from 1991 to 1993. We find that the most appropriate estimate of the effective population size of the captive-raised progeny is a variance estimate of effective population size standardized so that the number of released smolts returning to spawn was the same as the number of spawners used to produce the smolts originally. We have generated 10,000 random samples to simulate returns from these released progeny. The estimates of the effective population sizes in 1991, 1992, and 1993 were only 7.02, 19.07, and 7.74, respectively. We then determined limits on the effective population size of the natural run based on 0.1 and 0.333 of the run-size estimates. Using estimates of the captive proportion of the run, the minimum estimates of the effective population size of the overall run for the three years were 21.9, 127.3, and 39.0, and the maximum estimates were 61.6, 401.0, and 108.7. It does not appear that the hatchery program has reduced the overall effective population size. The run sizes in each year are extremely low, however, and it is possible that fish will be lost from this run in one of the years in the immediate future, making reestablishment of a healthy run even more difficult.     

Use of Linkage Disequilibrium Data to Estimate Effective Size of Hatchery and Natural Fish Populations

A primary parameter in the assessment of the viability of a population is its effective population size ( Ne ). Allozyme analysis of four groups of fishes provided data on linkage disequilibrium, which were then used to estimate Ne . The groups included hatchery samples of juvenile white seabass, Atractoscion nobilis , juvenile rainbow trout, Oncorhynchus mykiss , from the Shasta Hatchery, and juvenile chinook salmon, O. tshawytscha , from the Coleman National Fish Hatchery. The fourth sample consisted of juvenile chinook salmon from the threatened winter run in the upper Sacramento River. The groups of fish were chosen to represent different applications of the methodology to conservation of fishes. For a variety of reasons. Ne may be considerably lower than census counts of fish present in the parental populations. The Ne of the hatchery broodstock that produced the sample of juvenile white seabass was estimated to be approximately 10, although 25 adult white seabass were present in a mass spawning tank. Ne estimates for the parental populations of the Shasta and Coleman Hatchery samples were 35.8 and 132.5, respectively. The actual number of fish spawned at the Shasta Hatchery was approximately 40, whereas nearly 10,000 salmon were spawned at the Coleman Hatchery. The threatened winter run of chinook salmon had an estimated Ne of 85.5 and an approximate run size of 2000 salmon. The method of estimating effective population size from linkage disequilibrium data appears to result in realistic estimates of effective population size when adequate sample size and a sufficient number of polymorphic loci are available.     

Accounting for matching uncertainty in two stage capture–recapture experiments using photographic measurements of natural marks

We propose a Bayesian hierarchical modeling approach for estimating the size of a closed population from data obtained by identifying individuals through photographs of natural markings. We assume that noisy measurements of a set of distinctive features are available for each individual present in a photographic catalogue. To estimate the population size from two catalogues obtained during two different sampling occasions, we embed the standard two-stage $M_t$ capture–recapture model for closed population into a multivariate normal data matching model that identifies the common individuals across the catalogues. In addition to estimating the population size while accounting for the matching process uncertainty, this hierarchical modelling approach allows to identify the common individuals by using the information provided by the capture–recapture model. This way, our model also represents a novel and reliable tool able to reduce the amount of effort researchers have to expend in matching individuals. We illustrate and motivate the proposed approach via a real data set of photo-identification of narwhals. Moreover, we compare our method with a set of possible alternative approaches by using both the empirical data set and a simulation study.     

Extinction Rate Estimates for Plant Populations in Revisitation Studies: Importance of Detectability

Abstract:  Many researchers have obtained extinction-rate estimates for plant populations by comparing historical and current records of occurrence. A population that is no longer found is assumed to have gone extinct. Extinction can then be related to characteristics of these populations, such as habitat type, size, or species, to test ideas about what factors may affect extinction. Such studies neglect the fact that a population may be overlooked, however, which may bias estimates of extinction rates upward. In addition, if populations are unequally detectable across groups to be compared, such as habitat type or population size, comparisons become distorted to an unknown degree. To illustrate the problem, I simulated two data sets, assuming a constant extinction rate, in which populations occurred in different habitats or habitats of different size and these factors affected their detectability. The conventional analysis implicitly assumed that detectability equalled 1 and used logistic regression to estimate extinction rates. It wrongly identified habitat and population size as factors affecting extinction risk. In contrast, with capture-recapture methods, unbiased estimates of extinction rates were recovered. I argue that capture-recapture methods should be considered more often in estimations of demographic parameters in plant populations and communities.     

Estimating Population Size in a Continuous-time Removal Experiment with a Known Sub-population Size Ratio

We study a continuous-time removal model for estimating the population size for a population in which a sub-population size ratio is known. The maximum likelihood estimate and the optimal martingale estimate of the population size are obtained; these are shown to be equivalent. A comparison between the proposed estimator and the maximum likelihood estimate which ignores the information on the known size ratio is made, using a simulation study. The asymptotic variances of these two estimators are also obtained, and a comparison between them is made. The sensitivity of mis-specification of the known size ratio is examined. We also apply the corresponding discrete-time model to the proposed continuous-time setting, and study the efficiency of the corresponding discrete-time type estimator relative to the proposed estimator.     

Estimating Population Size of Elusive Animals with DNA from Hunter-Collected Feces: Four Methods for Brown Bears

Abstract:  Noninvasive genetic methods can be used to estimate animal abundances and offer several advantages over conventional methods. Few attempts have been made, however, to evaluate the accuracy and precision of the estimates. We compared four methods of estimating population size based on fecal sampling. Two methods used rarefaction indices and two were based on capture-mark-recapture (CMR) estimators, one combining genetic and field data. Volunteer hunters and others collected 1904 fecal samples over 2 consecutive years in a large area containing a well-studied population of brown bears ( Ursus arctos ). On our 49,000-km² study area in south-central Sweden, population size estimates ranged from 378 to 572 bears in 2001 and 273 to 433 bears in 2002, depending on the method of estimation used. The estimates from the best model in the program MARK appeared to be the most accurate, based on the minimum population size estimate from radio-marked bears in a subsection of our sampling area. In addition, MARK models included heterogeneity and temporal variation in detection probabilities, which appeared to be present in our samples. All methods, though, incorrectly suggested a biased sex ratio, probably because of sex differences in detection probabilities and low overall detection probabilities. The population size of elusive animals can be estimated reliably over large areas with noninvasive genetic methods, but we stress the importance of an adequate and well-distributed sampling effort. In cases of biased sampling, calibration with independent estimates may be necessary. We recommend that this noninvasive genetic approach, using the MARK models, be used in the future in areas where sufficient numbers of volunteers can be mobilized.