Confidence intervals for fish out-migration estimates using stratified trap efficiency methods

Marked fish placed above a smolt trap allow fisheries biologists to estimate the efficiency of the trap and hence to estimate the number of smolts migrating past the trap. We compared stratified maximum likelihood estimators and confidence intervals of total numbers of fish migrating to two other well-known estimators and associated confidence intervals using simulation. The Bailey adjusted Lincoln-Petersen estimator and its bootstrap confidence interval performed well overall.     

Density and population size estimates for North Cascade grizzly bears using DNA hair-sampling techniques

We used non-invasive DNA hair-sampling and catch per unit effort (CPUE: grizzly bears detected per 1000 trap nights) to estimate relative density and population size for a threatened grizzly bear population in the North Cascade Ecosystem of Washington and British Columbia. We used linear, logistic, and linear through the origin regression analyses to estimate the relationship between catch per unit effort and grizzly bear density for seven other grizzly populations. One grizzly bear was detected during 5304 trap nights (CPUE=0.19) over 3 years in the North Cascades. This CPUE was much lower than in the other seven populations, including two threatened grizzly populations in the Cabinet-Yaak and Selkirk Mountain Ecosystems. The logistic model (curvilinear relationship) best fit the data (R²=0.927), and yielded density and population size estimates of 0.15 bears/100 km² (90% CI=0.03-0.71) and six bears (90% CI=1-27), respectively. Natural recovery seems unlikely for the North Cascade grizzly bear population because the population has a high likelihood of extinction due to demographic and environmental stochastic effects associated with extremely small population numbers. We recommend population augmentation. DNA hair-sampling and catch per unit effort models can be a useful method to evaluate relative densities and numbers of animals in small, threatened grizzly bear populations when sample sizes are too small to yield traditional mark-recapture analysis.     

建立数学模型估测牧草产量研究

准确地估测草地产草量,为确定合理载畜量提供重要的科学依据,对维护草原生态平衡,合理安排畜牧业生产等具有重要的理论与现实意义。估算草地产草量的手段可分为野外测量法、气象模拟模型法和遥感模型法三大类。利用大量的野外实际调查资料,应用微积分原理,推导出估算草地产草量的数学模型,并与其它模型进行对比,探讨了该方法的可靠性及估算精度。最后,将模型推广应用于生产实践中以估测牧草产量。     

Estimating variograms of soil properties by the method-of-moments and maximum likelihood

Variograms of soil properties are usually obtained by estimating the variogram for distinct lag classes by the method‐of‐moments and fitting an appropriate model to the estimates. An alternative is to fit a model by maximum likelihood to data on the assumption that they are a realization of a multivariate Gaussian process. This paper compares the two using both simulation and real data. The method‐of‐moments and maximum likelihood were used to estimate the variograms of data simulated from stationary Gaussian processes. In one example, where the simulated field was sampled at different intensities, maximum likelihood estimation was consistently more efficient than the method‐of‐moments, but this result was not general and the relative performance of the methods depends on the form of the variogram. Where the nugget variance was relatively small and the correlation range of the data was large the method‐of‐moments was at an advantage and likewise in the presence of data from a contaminating distribution. When fields were simulated with positive skew this affected the results of both the method‐of‐moments and maximum likelihood. The two methods were used to estimate variograms from actual metal concentrations in topsoil in the Swiss Jura, and the variograms were used for kriging. Both estimators were susceptible to sampling problems which resulted in over‐ or underestimation of the variance of three of the metals by kriging. For four other metals the results for kriging using the variogram obtained by maximum likelihood were consistently closer to the theoretical expectation than the results for kriging with the variogram obtained by the method‐of‐moments, although the differences between the results using the two approaches were not significantly different from each other or from expectation. Soil scientists should use both procedures in their analysis and compare the results.     

A linear functional relationship model for circular data with an application to the assessment of ocean wave measurements

This article suggests a linear functional relationship model for comparing two sets of circular data subject to unobservable errors. Unlike the corresponding and relatively well-studied model for linear data, maximum likelihood estimation for this model is very complicated and no explicit solutions are possible. Using a numerical approximation, we are able to solve the likelihood equations approximately, and to obtain good approximations to the likelihood estimates of the parameters. The quality of our estimates and the feasibility of the estimation method are illustrated via simulation. By establishing a parallel with the model for linear data, we are able to explain the various problems occurring in the process of estimation and to substantiate our numerical results. The interest in the model arose in connection with the study of ocean wave data; an application to such data is also given.     

Improving density estimates for elusive carnivores: Accounting for sex-specific detection and movements using spatial capture–recapture models for jaguars in central Brazil

Owing to habitat conversion and conflict with humans, many carnivores are of conservation concern. Because of their elusive nature, camera trapping is a standard tool for studying carnivores. In many vertebrates, sex-specific differences in movements – and therefore detection by cameras – are likely. We used camera trapping data and spatially explicit sex-specific capture–recapture models to estimate jaguar density in Emas National Park in the central Brazilian Cerrado grassland, an ecological hotspot of international importance. Our spatially explicit model considered differences in movements and trap encounter rate between genders and the location of camera traps (on/off road). We compared results with estimates from a sex-specific non-spatial capture–recapture model. The spatial model estimated a density of 0.29 jaguars 100 km⁻² and showed that males moved larger distances and had higher trap encounter rates than females. Encounter rates with off-road traps were one tenth of those for on-road traps. In the non-spatial model, males had a higher capture probability than females; density was estimated at 0.62 individuals 100 km⁻². The non-spatial model likely overestimated density because it did not adequately account for animal movements. The spatial model probably underestimated density because it assumed a uniform distribution of jaguars within and outside the reserve. Overall, the spatial model is preferable because it explicitly considers animal movements and allows incorporating site-specific and individual covariates. With both methods, jaguar density was lower than reported from most other study sites. For rare species such as grassland jaguars, spatially explicit capture–recapture models present an important advance for informed conservation planning.     

Selection of Spatial-Temporal Lattice Models: Assessing the Impact of Climate Conditions on a Mountain Pine Beetle Outbreak

Insects are among the most significant indicators of a changing climate. Here we evaluate the impact of temperature, precipitation, and elevation on the tree-killing ability of an eruptive species of bark beetle in pine forests of British Columbia, Canada. We consider a spatial-temporal linear regression model and in particular, a new statistical method that simultaneously performs model selection and parameter estimation. This approach is penalized maximum likelihood estimation under a spatial-temporal adaptive Lasso penalty, paired with a computationally efficient algorithm to obtain approximate penalized maximum likelihood estimates. A simulation study shows that finite-sample properties of these estimates are sound. In a case study, we apply this approach to identify the appropriate components of a general class of landscape models which features the factors that propagate an outbreak. We interpret the results from ecological perspectives and compare our method with alternative model selection procedures.     

基于动态统计规则和景观格局特征的土地利用覆被空间模拟预测

土地利用覆被及其变化是一个区域土地资源可持续利用的状态信号。该文建立依据马尔柯夫链模型和最大似然概率原则的统计概率模型,将景观格局特征利用类别共生概率矩阵表达在模型中,其次采用动态统计来考虑不同位置处模型参数的局部化。通过在北京山区初步验证,考虑景观格局特征,模拟结果总精度提高2.4%,Kappa系数提高0.045。随估计参数局部化,模拟精度大幅度提高,总精度提高到90%以上。结果表明：该土地利用覆被模拟模型是可行的,具有所需要基本数据非常简单优点,免除数据收集处理以及关系量化困难等问题。     

Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood

This article considers logistic regression analysis of binary data that are measured on a spatial lattice and repeatedly over discrete time points. We propose a spatial-temporal autologistic regression model and draw statistical inference via maximum likelihood. Due to an unknown normalizing constant in the likelihood function, we use Monte Carlo to obtain maximum likelihood estimates of the model parameters and predictive distributions at future time points. We also use path sampling to estimate the unknown normalizing constant and approximate an information criterion for model assessment. The methodology is illustrated by the analysis of a dataset of mountain pine beetle outbreaks in western Canada.     

Modeling leaf area index from litter collection and tree data in a deciduous broadleaf forest

Leaf area index (LAI) is an important index in ecological and meteorological studies. The litter trap method is commonly used to measure LAI in deciduous forests. To reduce the time consumed in sorting leaf litterfall by species in the litter trap method, we developed four models to predict LAI using litter traps and tree census data. The local dominance model, which estimates the leaf litterfall amount of each species by their local dominance, predicted mean and spatial variability of LAI most accurately compared to the 2 models that did not take into account spatial heterogeneity of species distribution within a forest or the model that estimated litterfall amount from leaf dispersal function. Therefore, this model can be employed instead of sorting leaf litter by species. Furthermore, we found that leaf mass per area (LMA) of at least 10 dominant species are essential for accurate estimation of LAI. Present results suggest that spatial variability of LAI is mainly due to spatial variance of leaf litterfall followed by spatial heterogeneity of species distribution within a forest, and difference in LMA among species.     

农地产权制度安排与农户水土保持行为响应

 为揭示不同农地产权情况与农户水土保持行为响应的差别,探讨了农地产权中,各个要素对农户水土保持行为的影响,构建了农地产权与农户水土保持行为响应相互关系的逻辑模型,并根据农户调查数据,应用极大似然估计法,对该模型进行了估计。农地产权与农户水土保持决策逻辑模型的运行结果表明:在行政村内转包的权利、抵押权、有无土地租赁、农户对水土流失的感知、不同的地区对农户的水土保持决策有明显的影响。     

Sampling for ants in different-aged spruce forests: A comparison of methods

It is necessary to use special sampling method for studying of each animal group. However each method has its specificity and describes the studied community a bit differently. Three common methods for sampling of ants were compared: soil excavation, pitfall traps and baits. Ants were sampled in 25 patches in a large spruce forest in the Czech Republic during 2005 and 2006. Each patch represented one of five age classes (0–2, 3–5, 8–12, 26–41 and 85–105 years old), and the patches were located in five sites such that the five patches in each site formed a chronosequence. The percentage of variability in ants as explained by other sampling techniques was as follows: 60.0% of the variability in bait data was explained by pitfall trap data, while only 19.7% of the variability in pitfall trap data was explained by excavation data. Species and numbers of ants on baits were strongly affected by bait type: Myrmica species preferred honey whereas Formicinae preferred tuna bait. Assembly composition in pitfall traps varied significantly between study sites. The position of sampling site in landscape (location in the forest regardless of the immediate habitat type where the trap was located) hence strongly affected ants. Proportions of ants in particular methods were equal in the majority of cases. For excavation samples, ant numbers and species were more influenced by the immediate habitat than the wider habitat, but the opposite was true for samples from pitfall traps.     

Field testing of different synthetic attractants for mass trapping of common European cockchafer (Melolontha melolontha [L.], Coleoptera,Scarabaeidae) adults

Between 2012 and 2014, we tested the efficacy of different synthetic attractants for the purpose of massive trapping of common European cockchafer adults (Melolontha melolontha). The research took place in three different locations in Slovenia (Otlica, ?rni Vrh nad Idrijo, Cesta nad Ajdov??ino) during flight periods of adult beetles. In the period 2013–2014, we used, on the basis of the preliminary test results (2012), the following chemicals: toluquinone, cis–3–hexen–1-ol, ethyl acetate, toluquinone?+?cis–3 hexen–1-ol, and ethanol as a control. M. melolontha adults were most abundant in the location ?rni Vrh nad Idrijo; the traps in this location caught 18 times more of them than those in the first location and more than six times more than those in the third location. Male insects accounted for 73 to 87% of the trapped specimens. We confirmed the highest efficiency of cis–3–hexen–1-ol in 2013 (43.25?±?0.08 males/trap), as well as in 2014 (15.00?±?0.14 males/trap). On the basis of the trapped adult common European cockchafers and the simple economic analysis of applying different synthetic substances for attracting them, we found that independent application of cis-3-hexen-1-ol is the most efficient and cost-effective option for attracting the studied insect pest. Consequently, we recommend it as an attractant in the traps for massive trapping of adult common European cockchafers.     

Abundance and diversity of soil arthropods in olive grove ecosystem (Portugal): Effect of pitfall trap type

Soil arthropod biodiversity is an indicator of soil quality and can be studied using pitfall trapping. In this research, olive grove edaphic fauna was assessed at different sampling dates by comparing two different diameters (7 and 9 cm) and three different contents (empty, water and preservative) of pitfall traps in order to determine which type of pitfall trap is more efficient. Considering all pitfall trap types and sampling times, a total of 12,937 individual edaphic arthropods belonging to 11 taxa were recovered. Smaller traps with preservative collected significantly more individuals than the other pitfalls tested. Larger and empty traps collected significantly more spiders and traps with preservative collected more beetles. Smaller and empty traps collected fewer individuals than the other trap types. Both Shannon's diversity and Pielou's evenness indexes were higher in the larger and empty traps and richness was higher in the smaller traps filled with water. The study of myrmecocenosis was emphasised because olive grove soil fauna was numerically dominated by Formicidae (56.6% of all organisms captured) belonging to 12 genera and 24 species; Tapinoma nigerrimum, Messor barbarus, Cataglyphis hispanicus, Tetramorium semilaeve, Cataglyphis ibericus, Messor bouvieri and Camponotus cruentatus were the most abundant ant species. Traps with preservative reached the highest accumulation of species for a small number of pitfalls when compared with the other pitfalls studied and a sampling effort of 20 samples is apparently sufficient to sample the greater part of the ant species of the olive grove. From this study, it seems that traps with preservative are the best choice to use in further studies concerning the epiedaphic fauna of the olive grove.     

Dose Response Models with Natural Mortality and Random Effects

When fitting dose–response models to entomological data it is often necessary to take account of natural mortality and/or overdispersion. The standard approach to handle natural mortality is to use Abbott’s formula, which allows for a constant underlying mortality rate. Commonly used overdispersion models include the beta-binomial model, logistic-normal, and discrete mixtures. Here we extend the standard natural mortality model by including a random effect to account for overdispersion. Parameter estimation is based on a combined EM Newton–Raphson algorithm, which provides a simple framework for maximum likelihood estimation of the natural mortality model. We consider the application of this model to data from an experiment on the use of a virus (PhopGV) for the biological control of worm larvae (Phthorimaea operculella) in potatoes. For this natural mortality model with a random effect we introduce the likelihood ratio test, effective dose, and the use of a simulated residual envelope for model checking. Comparisons are made with an equivalent beta-binomial model. The procedures are implemented in the R system.     

基于改进CornerNet的水稻灯诱飞虱自动检测方法构建与验证

针对水稻灯诱昆虫图像中稻飞虱自动检测存在严重误检和漏检问题,提出一种基于改进CornerNet的水稻灯诱飞虱自动检测方法。由于稻飞虱个体在灯诱昆虫图像中所占区域比例极小,利用重叠滑动窗方法提高飞虱在图像检测区域中所占比例,提高2种稻飞虱(白背飞虱和褐飞虱)的检测率和避免滑动窗边界造成的目标漏检。针对CornerNet存在角点匹配不准确导致检测框冗余问题,利用检测框抑制方法去除冗余检测框。对灯诱昆虫图像进行稻飞虱检测,结果表明,该研究提出的基于改进CornerNet的水稻灯诱飞虱自动检测方法对2种稻飞虱检测的平均精确率和召回率分别为95.53%和95.50%,有效地提高了灯诱昆虫图像中稻飞虱的检测效果,可用于智能虫情测报灯的灯诱昆虫图像中白背飞虱和褐飞虱的智能测报。     

Wood, nematodes, and the nematode-trapping fungus Arthrobotrys oligospora

Researchers have proposed that Arthrobotrys oligospora and related fungi trap soil nematodes to obtain nitrogen and thereby compete saprophytically for carbon and energy in nitrogen-poor environments, including litter and wood. The current study tested two hypotheses concerning this model. The first was that wood decomposition would be enhanced if both large numbers of nematodes (a potential nitrogen supply) and A. oligospora (a cellulolytic organism that can use that N supply) were present. The second was that A. oligospora trapping activity would increase if large numbers of nematodes were added to soil containing abundant carbon (a wood dowel or chip). Although the first hypothesis was supported by an in vitro experiment on agar (A. oligospora degraded much more wood when nematodes were present), neither hypothesis was supported by an experiment in vials containing field soil. In soil, wood decomposition was unaffected by the addition of A. oligospora or large numbers of nematodes. Whereas A. oligospora trapped virtually all nematodes added to agar cultures, it trapped few or no nematodes added to soil. Given that the fungal isolate was obtained from the same soil and that the fungus increased to large numbers (>1×10³ propagules g⁻¹ soil), the failure of A. oligospora to trap nematodes in soil is difficult to explain. Soil nitrate levels, however, were high (71 mg kg⁻¹), and it is possible that with lower nitrate levels, trapping in soil might be stimulated by wood and nematodes.     

Model-Averaged Profile Likelihood Intervals

Model-averaging is commonly used as a means of allowing for model uncertainty in parameter estimation. In the frequentist framework, a model-averaged estimate of a parameter is the weighted mean of the estimates from each of the candidate models, the weights typically being chosen using an information criterion. Current methods for calculating a model-averaged confidence interval assume approximate normality of the model-averaged estimate, i.e., they are Wald intervals. As in the single-model setting, we might improve the coverage performance of this interval by a one-to-one transformation of the parameter, obtaining a Wald interval, and then back-transforming the endpoints. However, a transformation that works in the single-model setting may not when model-averaging, due to the weighting and the need to estimate the weights. In the single-model setting, a natural alternative is to use a profile likelihood interval, which generally provides better coverage than a Wald interval. We propose a method for model-averaging a set of single-model profile likelihood intervals, making use of the link between profile likelihood intervals and Bayesian credible intervals. We illustrate its use in an example involving negative binomial regression, and perform two simulation studies to compare its coverage properties with the existing Wald intervals.     

Risks of decline and extinction of the endangered Amsterdam albatross and the projected impact of long-line fisheries

The Amsterdam albatross (Diomedea amsterdamensis) is one of the rarest bird species of world avifauna, consisting of a single population in the upland plateau of Amsterdam Island (SE Indian Ocean). All breeding birds of the population are today banded and a monitoring program involving mark-recapture procedures has been carried out continuously over the past 16 years. We present the first estimate of risk of decline for the Amsterdam albatross using a stochastic matrix population model, and evaluate the extent to which the measurement errors in demographic estimates may affect the baseline conservation assessment. We also estimate the potential effect that resumption of long-line fisheries in the vicinity of Amsterdam Island (one the alleged causes for its low numbers in the recent past) may have on the persistence of this population. Our results indicate that, in the absence of any impact of long-line fisheries, the Amsterdam albatross is unlikely to experience a decline larger than 20% of the current population abundance over the next 50 years. Our results point out the difficulty to assess with certainty the extinction risk of small populations despite the availability of long term data on their demography. They suggest that a very cautious approach should be taken for the preservation of small populations of long-lived species that cannot sustain any level of incidental by-catch. Any new long-line fishery resuming in the foraging range of the Amsterdam albatross, but especially close to Amsterdam Island, may rapidly put this species at risk of extinction.     

Estimating species number under an inconvenient abundance model

Estimating the number of species in a biological community based on a multinomial sample of individual organisms is a classical problem in statistical ecology. A central issue in parametric estimation is the specification of a model of the relative abundances of species given their number. A common approach to this problem is to assume that relative abundances follow a symmetric Dirichlet distribution. This is mathematically convenient but is unconnected to work by ecologists on abundance distributions in real communities. In this article we describe ML estimation based on the sequential broken stick model that has been proposed for abundances. This model is defined mechanistically, requiring that the likelihood be approximated numerically. For this to be feasible, the likelihood must be based on a small number of summary statistics. We present simulation results that show that the observed number of species and the observed number of species represented by a single individual is a reasonable set of summary statistics on which to base estimation. We apply the method to two published data sets, one involving insect species on Mount Kenya and the other involving spider species in an Appalachian forest.