中华姬鼠巢区的研究

中华姬鼠是亚热带山地常绿阔叶林内的优势种。本文报道了用标志重捕流放法,研究该鼠不同个体的巢区大小、相互关系以及在样地上的分布等一些生态学资料。     

Tests for independence between marks and points of a marked point process

A convenient assumption while modeling a marked point process is that the observations (i.e., marks) and the locations (i.e., points) are independent. We propose new graphical and formal testing approaches to test for this assumption. The proposed graphical procedures are easy to obtain and can be used to diagnose the nature and range of dependence between marks and points. The formal testing procedures require only minimal conditions on marks and thus can be applied to a variety of settings. We illustrate these procedures through a simulation study and an application to some real data.     

Demographic versus genetic dispersal measures

Quantifying dispersal, a fundamental biological process, is far from simple. Here, both direct and indirect methods were employed to estimate dispersal in an endangered butterfly species. A high and significant correlation between the dispersal patterns, generated by an inverse power function fitted to capture-mark-recapture (CMR) data on the one hand, and population genetic analyses on the other hand, was observed. Stepping-stone type movements were detected by both methods, evidence for the importance of connectivity in the studied metapopulation. These results are particularly relevant to those population and conservation biology studies where quantifying dispersal is essential for the elaboration of successful management actions.     

Reduction of Banana Weevil Populations Using Different Formulations of the Entomopathogenic Fungus Beauveria bassiana

One of the major constraints for banana production in Uganda is the banana weevil, Cosmopolites sordidus (Germar), (Coleoptera: Curculionidae). Investigations were carried out to evaluate the efficacy of maize, soil-based and oil formulations of an indigenous isolate of Beauveria bassiana for the control of the banana weevil. Weekly trapping of weevils over a 9-month monitoring period showed significant reduction in unmarked and marked weevil population in B. bassiana treated plots. Application of maize formulation at 2 ×10 15 conidia ha -1 proved most effective, reducing the weevil populations by 63-72% within 8 weeks after a single application. The soil based formulation at 2 ×10 14 conidia ha -1 was intermediate while the oil formulation at 6 ×10 15 conidial ha -1 was least effective. Trapping efficiency declined in B. bassiana treated and untreated banana plots but was greatest in the latter.     

Continuous Covariates in Mark‐Recapture‐Recovery Analysis: A Comparison of Methods

Summary Time varying, individual covariates are problematic in experiments with marked animals because the covariate can typically only be observed when each animal is captured. We examine three methods to incorporate time varying, individual covariates of the survival probabilities into the analysis of data from mark‐recapture‐recovery experiments: deterministic imputation, a Bayesian imputation approach based on modeling the joint distribution of the covariate and the capture history, and a conditional approach considering only the events for which the associated covariate data are completely observed (the trinomial model). After describing the three methods, we compare results from their application to the analysis of the effect of body mass on the survival of Soay sheep (Ovis aries) on the Isle of Hirta, Scotland. Simulations based on these results are then used to make further comparisons. We conclude that both the trinomial model and Bayesian imputation method perform best in different situations. If the capture and recovery probabilities are all high, then the trinomial model produces precise, unbiased estimators that do not depend on any assumptions regarding the distribution of the covariate. In contrast, the Bayesian imputation method performs substantially better when capture and recovery probabilities are low, provided that the specified model of the covariate is a good approximation to the true data‐generating mechanism.     

Incorporating Genotype Uncertainty into Mark–Recapture-Type Models For Estimating Abundance Using DNA Samples

Summary .  Sampling DNA noninvasively has advantages for identifying animals for uses such as mark–recapture modeling that require unique identification of animals in samples. Although it is possible to generate large amounts of data from noninvasive sources of DNA, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. A major source of error is allelic dropout, which is failure of DNA amplification at one or more loci. This has the effect of heterozygous individuals being scored as homozygotes at those loci as only one allele is detected. If errors go undetected and the genotypes are naively used in mark–recapture models, significant overestimates of population size can occur. To avoid this it is common to reject low-quality samples but this may lead to the elimination of large amounts of data. It is preferable to retain these low-quality samples as they still contain usable information in the form of partial genotypes. Rather than trying to minimize error or discarding error-prone samples we model dropout in our analysis. We describe a method based on data augmentation that allows us to model data from samples that include uncertain genotypes. Application is illustrated using data from the European badger ( Meles meles ).     

Parameter redundancy in discrete state‐space and integrated models

Discrete state‐space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state‐space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state‐space models using discrete analogues of methods for continuous state‐space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant.