Composition of the gum from Combretum paniculatum and four other gums which are not permitted food additives.

Only three gum exudates are permitted for pharmaceutical and food use by international regulatory authorities, viz. gum tragacanth (Asiatic Astragalus spp.), gum karaya (Sterculia spp.) and gum arabic [Acacia senegal (L.) Willd.], but a wide range of other tree exudates is used for a variety of uses in their countries of origin. This paper presents analytical data for the gum exudates from Atalaya hemiglauca, Cassine aethiopica, Combretum paniculatum, Sclerocarya birrea, and Pseudocedrela kotschyi. These gums may have local technological applications, but are not recommended for addition to foodstuffs.     

Aspects of the dynamics of the seed-banks and seedling populations of Acacia victoriae and Cassia spp. in arid western New South Wales

Acacia victoriae, Cassia nemophila and C. phyllodinea are shrubs which occur throughout large areas of arid and semi-arid Australia. This paper examines aspects of the dynamics of the seed-banks and seedling populations of these plants to determine their influence upon recruitment frequency. Seed-banks in the soil are large, ranging from 50 to 3900 seeds m^-2for A. victoriae and from 5 to 400 seeds m^-2for Cassia spp. A large proportion (ca. 80%) of these seeds are viable. Seedling densities soon after emergence range up to 25.7 m^-2for A. victoriae and up to 21.7 m^-2for Cassia spp. Any individual cohort of seedlings represents only a small proportion (< 6%) of the total seed-bank. Most viable seeds fail to germinate even when adequate moisture is available. Seedling mortality rates are high (ca. 100%) during the first 12 months after germination. If recruitment of these shrubs is intermittent, it is most likely a result of low seedling survival rather than infrequent germination.     

Evaluating sampling designs by computer simulation: a case study with the Missouri bladderpod

To effectively manage rare populations, accurate monitoring data are critical. Yet many monitoring programs are initiated without careful consideration of whether chosen sampling designs will provide accurate estimates of population parameters. Obtaining accurate estimates is especially difficult when natural variability is high, or limited budgets determine that only a small fraction of the population can be sampled. The Missouri bladderpod, Lesquerella filiformis Rollins, is a federally threatened winter annual that has an aggregated distribution pattern and exhibits dramatic interannual population fluctuations. Using the simulation program SAMPLE, we evaluated five candidate sampling designs appropriate for rare populations, based on 4 years of field data: (1) simple random sampling, (2) adaptive simple random sampling, (3) grid-based systematic sampling, (4) adaptive grid-based systematic sampling, and (5) GIS-based adaptive sampling. We compared the designs based on the precision of density estimates for fixed sample size, cost, and distance traveled. Sampling fraction and cost were the most important factors determining precision of density estimates, and relative design performance changed across the range of sampling fractions. Adaptive designs did not provide uniformly more precise estimates than conventional designs, in part because the spatial distribution of L. filiformis was relatively widespread within the study site. Adaptive designs tended to perform better as sampling fraction increased and when sampling costs, particularly distance traveled, were taken into account. The rate that units occupied by L. filiformis were encountered was higher for adaptive than for conventional designs. Overall, grid-based systematic designs were more efficient and practically implemented than the others. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Effects of fire on central Australian rangelands. II Changes in tree and shrub populations

The effect of a summer fire or a winter fire on tree and shrub populations in two rangeland vegetation types in central Australia was investigated. Changes in population structure of two major species occurred regardless of the season of burn. The decrease in overall density of Acacia aneura, Eremophila gilesii and Cassia spp. was similar for summer and winter fire treatments: greater reduction of E. latrobei followed the summer burn. Higher fire line intensity caused greater mortality in all species but enhanced germination in A. aneura, a potentially ‘weedy’ species. Winter fires appear to be preferable for management of woody plants in the particular vegetation types studied.     

Development and economic evaluation of spatial sampling plans for corn rootworm Diabrotica spp. (Col., Chrysomelidae) adults

Abstract  To develop spatial sampling plans for corn rootworm ( Diabrotica spp.) adults, their spatial distributions were characterized and economics of sampling plans were evaluated by comparing sampling costs between spatial and conventional (non-spatial) sampling plans. Semivariogram modelling and spatial by with distance indices showed that corn rootworm adults were significantly (P < 0.05) aggregated at peak population densities and any two samples were spatially correlated within approximately 45 m, with 39–90% of the variability explained by spatial dependence. Sampling costs for spatial sampling plans linearly increased as the sampling distance decreased and exponentially increased as the field size increased. Although sampling costs for non-spatial sampling plans were generally lower, spatial sampling plans could be more economical when the mean insect density became lower and the field size became smaller. This study demonstrated that spatial sampling plans could be optimized to minimize the sampling costs and maximize the spatial resolution.     

Spatial analyses of ecological count data: A density map comparison approach

Analysing spatial patterns of population distributions may help to infer the decisive underlying ecological processes. Here we propose a method adapted to the spatial analysis of count data. Named MAPCOMP (MAP COMParison), it is based on the calculation of a formal distance, the Hellinger distance, between the density map of counts and the density map of sampling effort. Statistical tests of spatial homogeneity are based on count permutations across sampling sites and on valuable properties of the Hellinger distance. We assessed the efficiency of MAPCOMP by simulating different types and locations of clusters of individuals and compared its performance to the classical red–blue SADIE method, used as a reference. The two methods were also compared with respect to counts of codling moth larvae in orchards. Thanks to its better theoretical properties than SADIE, MAPCOMP was efficient in detecting spatial inhomogeneity when clusters were located on square or elongated spatial domains and more or less close to the edges, even for small sample sizes. It also appeared not very sensitive to edge effects. Another advantage of MAPCOMP is a bandwidth parameter that allows assessing the spatial extent of heterogeneity, if any.     

Performance of individual vs. group sampling for inferring dispersal under isolation‐by‐distance

Models of isolation‐by‐distance formalize the effects of genetic drift and gene flow in a spatial context where gene dispersal is spatially limited. These models have been used to show that, at an appropriate spatial scale, dispersal parameters can be inferred from the regression of genetic differentiation against geographic distance between sampling locations. This approach is compelling because it is relatively simple and robust and has rather low sampling requirements. In continuous populations, dispersal can be inferred from isolation‐by‐distance patterns using either individuals or groups as sampling units. Intrigued by empirical findings where individual samples seemed to provide more power, we used simulations to compare the performances of the two methods in a range of situations with different dispersal distributions. We found that sampling individuals provide more power in a range of dispersal conditions that is narrow but fits many realistic situations. These situations were characterized not only by the general steepness of isolation‐by‐distance but also by the intrinsic shape of the dispersal kernel. The performances of the two approaches are otherwise similar, suggesting that the choice of a sampling unit is globally less important than other settings such as a study's spatial scale.     

Use of the regression of mean crowding on mean density for estimating sample size and the transformation of data for the analysis of variance

An approximate method for estimating the sample size in simple random sampling and a systematic way of transformation of sample data are derived by using the parameters α and β of the regression of mean crowding on mean density in the spatial distribution per quadrat of animal populations (Iwao , 1968). If the values of α and β have been known for the species concerned, the sample size needed to attain a desired precision can be estimated by simply knowing the approximate level of mean density of the population to be sampled. Also, an appropriate variance stabilizing transformation of sample data can be obtained by the method given here without restrictions on the distribution pattern of the frequency counts.     

Comparison of density and basal area estimation of mountain natural forests based on distance-based sampling methods in Zhejiang,China

ObjectiveThe sustainable development of forest ecology and forest management practices is inseparable from the support of forest surveys. Different sampling methods have an unavoidable impact on the collection of natural community characteristic information. An appropriate method reduces the cost of the investigation to the maximum degree under the premise of ensuring accuracy. Distance-based sampling methods are widely used because of their excellent performance in estimating forest population characteristics. The purpose of this study is to compare and find an efficient sampling method of natural broad-leaved forests in mountainous areas of Zhejiang, China, which is of great significance to large-scale field survey practice in similar areas.MethodOur study used census survey data from fixed monitoring sample plots of natural broad-leaved forest in Wuyanling National Nature Reserve, Zhejiang, China as an example and simulated different distance-based sampling methods, including n-tree distance (NTD), point-centered quarter (PCQ), and T-square (Ts), combined with several estimators to estimate the stand density and basal area. The results were compared with the actual mean values of the 100% survey.ResultWe found that different sampling methods and estimators significantly influenced the results. NTD1 overestimated both the stand density and basal area, while NTD2 performed the best, with the lowest RMSE. Secondary performance was obtained with Ts3, Ts5, and Ts6, with small RMSEs of density and basal area. The RMSEs of the PCQ and Ts sampling methods based on a single distance were all large.ConclusionThe NTD sampling method with the NTD2 estimator is recommended to estimate the stand density and basal area for field investigation of natural forests in the Zhejiang mountainous area.     

Spatial distribution of the cereal leaf beetle (Coleoptera: Chrysomelidae) in wheat

A 2-yr study was conducted in wheat fields in South Carolina involving weekly sampling of cereal leaf beetle, Oulema melanopus (L.). In each of the six fields of this study, temporal patterns showed two distinct peaks in March and in May of adult O. melanopus. Populations decreased as wheat plants matured. In 2009, larval populations had one peak in April in between the two adult peaks. The χ(2) statistics for observed and Poisson predicted distributions of O. melanopus indicated nonrandom distribution for adults and larvae. In addition, the values of I(D) were >1 for adults and larvae in both years across sampling dates. These results indicate that the sampling distributions of both adult and larval populations of O. melanopus were aggregated. Slopes of Taylor power's law (b) and patchiness regressions (β) were significantly (P < 0.05) different than one in all cases, except for b in 2008 for adults. Across sampling dates, the distance from field border had a significant effect on adult O. melanopus in both years, but not on larval O. melanopus. Densities of adult O. melanopus were greatest at 0 m (the field edge), and decreased at 5-25 m from the field edge. The inverted distance weighted interpolation method showed considerable levels of spatial variability in densities within fields. High densities along the edge of wheat fields suggests that localized control methods in wheat may be effective in reducing migration of O. melanopus and damage in corn, Zea mays L.     

Nest distribution shaping within-stream variation in Atlantic salmon juvenile abundance and competition over small spatial scales

1. Spatial heterogeneity in population density is predicted to have important effects on population characteristics, such as competition intensity and carrying capacity. Patchy breeding distributions will tend to increase spatial heterogeneity in population density, whereas dispersal from breeding patches will tend to decrease it. The potential for dispersal to homogenize densities is likely to differ both among organisms (e.g. plants vs. mobile animals) and throughout ontogeny (e.g. larvae vs. adults). However, for mobile organisms, experimental studies of the importance of breeding distributions from the wild are largely lacking. 2. In the present study, experimental manipulations replicated over eight natural streams and 2 years enabled us to test for effects of the distribution of Atlantic salmon eggs over spatial scales which are relevant to local interactions among individuals. Artificial nests were placed along 250 m study reaches at one of two levels of nest dispersion - patchy (two nests per stream) and dispersed (10 nests per stream) - while holding total egg density (eggs m(-2) stream area) constant. 3. Nest dispersion had significant effects on the spatial distribution of the resulting juveniles in their first summer. Patchy nest distributions resulted in a highly right-skewed frequency distribution of local under-yearling densities (among 25 m sampling sections), as sample sections adjacent to the nest sites had relatively high densities. In contrast, dispersed nest distributions yielded approximately normal density distributions. Sections with high relative densities in the patchy nest distribution treatments also had relatively small juvenile body sizes, and patchy egg distribution appeared to produce a higher redistribution of individuals from the first to the second juvenile growth season than the dispersed distribution. 4. Because patchy breeding distribution combined with limited early dispersal can create spatial variation in density over scales directly relevant for individual interactions, this will be one important component in determining mean levels of early juvenile competition and its spatial variation within populations. Assuming random or ideal-free distribution of individuals may therefore underestimate the mean level of density experienced by juveniles over surprisingly small spatial scales (orders of magnitude smaller than total spatial extent of populations), even for mobile organisms.     

Estimating abundance and spatial distribution patterns of the bubble crab Dotilla fenestrata (Crustacea: Brachyura)

Abstract The bubble crab Dotilla fenestrata forms very dense populations on the sand flats of the eastern coast of Inhaca Island, Mozambique, making it an interesting biological model to examine spatial distribution patterns and test the relative efficiency of common sampling methods. Due to its apparent ecological importance within the sandy intertidal community, understanding the factors ruling the dynamics of Dotilla populations is also a key issue. In this study, different techniques of estimating crab density are described, and the trends of spatial distribution of the different population categories are shown. The studied populations are arranged in discrete patches located at the well‐drained crests of nearly parallel mega sand ripples. For a given sample size, there was an obvious gain in precision by using a stratified random sampling technique, considering discrete patches as strata, compared to the simple random design. Density average and variance differed considerably among patches since juveniles and ovigerous females were found clumped, with higher densities at the lower and upper shore levels, respectively. Burrow counting was found to be an adequate method for large‐scale sampling, although consistently underestimating actual crab density by nearly half. Regression analyses suggested that crabs smaller than 2.9 mm carapace width tend to be undetected in visual burrow counts. A visual survey of sampling plots over several patches of a large Dotilla population showed that crab density varied in an interesting oscillating pattern, apparently following the topography of the sand flat. Patches extending to the lower shore contained higher densities than those mostly covering the higher shore. Within‐patch density variability also pointed to the same trend, but the density increment towards the lowest shore level varied greatly among the patches compared.     

Fine-scale genetic structure and gene dispersal inferences in 10 neotropical tree species

The extent of gene dispersal is a fundamental factor of the population and evolutionary dynamics of tropical tree species, but directly monitoring seed and pollen movement is a difficult task. However, indirect estimates of historical gene dispersal can be obtained from the fine-scale spatial genetic structure of populations at drift-dispersal equilibrium. Using an approach that is based on the slope of the regression of pairwise kinship coefficients on spatial distance and estimates of the effective population density, we compare indirect gene dispersal estimates of sympatric populations of 10 tropical tree species. We re-analysed 26 data sets consisting of mapped allozyme, SSR (simple sequence repeat), RAPD (random amplified polymorphic DNA) or AFLP (amplified fragment length polymorphism) genotypes from two rainforest sites in French Guiana. Gene dispersal estimates were obtained for at least one marker in each species, although the estimation procedure failed under insufficient marker polymorphism, limited sample size, or inappropriate sampling area. Estimates generally suffered low precision and were affected by assumptions regarding the effective population density. Averaging estimates over data sets, the extent of gene dispersal ranged from 150 m to 1200 m according to species. Smaller gene dispersal estimates were obtained in species with heavy diaspores, which are presumably not well dispersed, and in populations with high local adult density. We suggest that limited seed dispersal could indirectly limit effective pollen dispersal by creating higher local tree densities, thereby increasing the positive correlation between pollen and seed dispersal distances. We discuss the potential and limitations of our indirect estimation procedure and suggest guidelines for future studies.     

基于空间结构调查的林分密度估计

利用林分空间结构抽样调查技术,采用测量抽样点到其第k株最近相邻木的距离(距离法)进行密度估计,分别选取k=4和k=6两种距离调查方法进行分析,并对Prodan、Persson、Thompson 3种不同密度估计方法的预估能力进行检验.结果表明：不同预估方法的预估能力受林木水平分布格局影响.Prodan法在均匀分布的林分中有较强的预估能力,随着分布格局聚集性增加会产生越来越大的偏差;Persson计算法在均匀及随机分布的林分中产生正偏差,但随着分布格局聚集性增加产生的相对误差接近0,预估能力增强;Thompson计算法对随机或接近随机分布的林分有较强的预估能力,而在均匀分布和聚集分布的格局中分别产生正偏差和负偏差.抽样点为49个时,选择6株木与4株木预估能力无显著差异,因此,密度估计可与选取4株相邻木的空间结构参数调查整合在一起.     

Improved nonparametric estimation of the optimal diagnostic cut‐off point associated with the Youden index under different sampling schemes

A diagnostic cut‐off point of a biomarker measurement is needed for classifying a random subject to be either diseased or healthy. However, the cut‐off point is usually unknown and needs to be estimated by some optimization criteria. One important criterion is the Youden index, which has been widely adopted in practice. The Youden index, which is defined as the maximum of (sensitivity + specificity ?1), directly measures the largest total diagnostic accuracy a biomarker can achieve. Therefore, it is desirable to estimate the optimal cut‐off point associated with the Youden index. Sometimes, taking the actual measurements of a biomarker is very difficult and expensive, while ranking them without the actual measurement can be relatively easy. In such cases, ranked set sampling can give more precise estimation than simple random sampling, as ranked set samples are more likely to span the full range of the population. In this study, kernel density estimation is utilized to numerically solve for an estimate of the optimal cut‐off point. The asymptotic distributions of the kernel estimators based on two sampling schemes are derived analytically and we prove that the estimators based on ranked set sampling are relatively more efficient than that of simple random sampling and both estimators are asymptotically unbiased. Furthermore, the asymptotic confidence intervals are derived. Intensive simulations are carried out to compare the proposed method using ranked set sampling with simple random sampling, with the proposed method outperforming simple random sampling in all cases. A real data set is analyzed for illustrating the proposed method.     

Genetic connectivity and diversity in inselberg populations of Acacia woodmaniorum,a rare endemic of the Yilgarn Craton banded iron formations

Historically rare plant species with disjunct population distributions and small population sizes might be expected to show significant genetic structure and low levels of genetic diversity because of the effects of inbreeding and genetic drift. Across the globe, terrestrial inselbergs are habitat for rich, often rare and endemic flora and are valuable systems for investigating evolutionary processes that shape patterns of genetic structure and levels of genetic diversity at the landscape scale. We assessed genetic structure and levels of genetic diversity across the range of the historically rare inselberg endemic Acacia woodmaniorum. Phylogeographic and genetic structure indicates that connectivity is not sufficient to produce a panmictic population across the limited geographic range of the species. However, historical levels of gene flow are sufficient to maintain a high degree of adaptive connectivity across the landscape. Genetic diversity indicates gene flow is sufficient to largely counteract any negative genetic effects of inbreeding and random genetic drift in even the most disjunct or smallest populations. Phylogeographic and genetic structure, a signal of isolation by distance and a lack of evidence of recent genetic bottlenecks suggest long-term stability of contemporary population distributions and population sizes. There is some evidence that genetic connectivity among disjunct outcrops may be facilitated by the occasional long distance dispersal of Acacia polyads carried by insect pollinators moved by prevailing winds.     

Linking biodiversity patterns by autocorrelated random sampling

Biodiversity macroecology deals with the commonly measured variables of abundance, distribution, occupancy, and range size across two scales: the local (or α) and regional (γ). There are ca. 15 patterns consisting of the frequency distributions of the variables, variables as a function of area or sample size, and interrelationships between variables that appear to be very general if not close to universal. A number of links can be drawn between these patterns. In particular, I show that local communities can be seen as random samples of the regional pool, but only as a special form of sampling that is autocorrelated due to the spatial clumping of individuals within a species. I describe two distinct sets of mathematical machinery that can start with the regional species abundance distribution and then predict local species richness, local species abundance distributions, and β-diversity (in the form of species area relationships or decay of similarity with distance). I conclude by examining some of the implications of the fact that biodiversity patterns are linked by autocorrelated sampling.     

Adaptive Cluster Sampling in the Context of Restoration

Abundance is an important population state variable for monitoring restoration progress. Efficient sampling often proves difficult, however, when populations are sparse and patchily distributed, such as early after restoration planting. Adaptive cluster sampling (ACS) can help by concentrating search effort in high density areas, improving the encounter rate and the ability to detect a population change over time. To illustrate the problem, I determined conventional design sample sizes for estimating abundance of 12 natural populations and 24 recently planted populations (divided among two preserves) of Lupinus perennis L. (wild blue lupine). I then determined the variance efficiency of ACS relative to simple random sampling at fixed effort and cost for 10 additional planted populations in two habitats (field vs. shrubland). Conventional design sample sizes to estimate lupine stem density with 10% or 20% margins of error were many times greater than initial sample size and would require sampling at least 90% of the study area. Differences in effort requirements were negligible for the two preserves and natural versus planted populations. At fixed sample size, ACS equaled or outperformed simple random sampling in 40% of populations; this shifted to 50% after correcting for travel time among sample units. ACS appeared to be a better strategy for inter‐seeded shrubland habitat than for planted field habitat. Restoration monitoring programs should consider adaptive sampling designs, especially when reliable abundance estimation under conventional designs proves elusive.     

Relationship between microspatial population genetic structure and habitat heterogeneity in Pomatias elegans (O.F. Müller 1774) (Caenogastropoda, Pomatiasidae)

In the present study the population genetic structure of the terrestrial snail Pomatias elegans was related to habitat structure on a microspatial scale. The genetic variability of 1607 individuals from 51 sampling sites in five different populations in Provence, France, was studied with an allozyme marker using population genetic methods, Mantel tests and spatial autocorrelation techniques were applied to different connectivity networks accounting for the structural features of the landscape. It is suggested that the population structure is, to a large extent, a function of the habitat quality, quantified as population density, and of the spatial arrangement of the habitat in the landscape and not of the geographical distance per se . In fragmented habitats, random genetic drift was the prevailing force for sampling sites separated by a few hundred meters.  © 2002 The Linnean Society of London, Biological Journal of the Linnean Society , 2002, 76 , 565–575.     

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

Summary .  We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike's information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.