Obtaining species: sample size considerations

Suppose fish are to be sampled from a stream. A fisheries biologist might ask one of the following three questions: ‘How many fish do I need to catch in order to see all of the species?’, ‘How many fish do I need to catch in order to see all species whose relative frequency is more than 5%?’, or ‘How many fish do I need to catch in order to see a member from each of the species A, B, and C?’. This paper offers a practical solution to such questions by setting a target sample size designed to achieve desired results with known probability. We present three sample size methods, one we call ‘exact’ and the others approximate. Each method is derived under assumed multinomial sampling, and requires (at least approximate) independence of draws and (usually) a large population. The minimum information needed to compute one of the approximate methods is the estimated relative frequency of the rarest species of interest. Total number of species is not needed. Choice of a sample size method depends largely on available computer resources. One approximation (called the ‘Monte Carlo approximation’) gets within ±6 units of exact sample size, but usually requires 20–30 minutes of computer time to compute. The second approximation (called the ‘ratio approximation’) can be computed manually and has relative error under 5% when all species are desired, but can be as much as 50% or more too high when exact sample size is small. Statistically, this problem is an application of the ‘sequential occupancy problem’. Three examples are given which illustrate the calculations so that a reader not interested in technical details can apply our results.     

Market responses to hurricanes

This paper uses one of the strongest hurricanes to hit the US, Andrew in 1992, to define a quasi-random experiment that permits estimation of the responses of housing values to information about new hurricanes. Lee County, Florida did not experience damage from Andrew. The storm was a “near-miss.” We hypothesize that Andrew conveyed risk information to homeowners in the county. A difference-in-differences (DND) framework identifies the effect of this information on property values in areas likely to experience significant storm damage. The DND findings indicate at least a 19 percent decline in property values.     

Multistate models for monitoring individual trees in permanent observation plots

This interdisciplinary research on forest ecosystems begins with some characteristics of ecosystems which are the basis for the derivation of statistical models for the development and vitality of trees. Several ecological problems which could be solved by longitudinal studies are mentioned. Statistical methods for the evaluation of the crowns of spruce trees (Picea abies Karst) in three permanent observation plots in Switzerland are described. In particular, the time-dependent proportional odds model and a transitional model are used. Through application of these multistate models the data give information on the dependence of an ordered categorical response variable on covariates characterizing the ecosystem. The response variable is observed through infrared aerial photographs. This monitoring system gives insight into the dynamic behaviour of the forest ecosystem. The need for more eco-systematically motivated statistical research using longitudinal studies is identified.     

A simple approach to modeling microbial biomass in the rhizosphere

Microorganisms make an important contribution to the degradation of contaminants in bioremediation as well as in phytoremediation. An accurate estimation of microbial concentrations in the soil would be valuable in predicting contaminant dissipation during various bioremediation processes. A simple modeling approach to quantify the microbial biomass in the rhizosphere was developed in this study. Experiments were conducted using field column lysimeters planted with Eastern gamagrass. The microbial biomass concentrations from the rhizosphere soil, bulk soil, and unplanted soil were monitored for six months using an incubation–fumigation method. The proposed model was applied to the field microbial biomass data and good correlation between simulated and experimental data was achieved. The results indicate that plants increase microbial concentrations in the soil by providing root exudates as growth substrates for microorganisms. Since plant roots are initially small and do not produce large quantities of exudates when first seeded, the addition of exogenous substrates may be needed to increase initial microbial concentrations at the start of phytoremediation projects.     

Dynamics of soil organic matter in primary and secondary forest succession on sandy soils in The Netherlands: An application of the ROMUL model

We applied the simulation model ROMUL of soil organic matter dynamics in order to analyse and predict forest soil organic matter (SOM) changes following stand growth and also to identify gaps of data and modelling problems. SOM build-up was analysed (a) from bare sand to forest soil during a primary succession in Scots pine forest and (b) on mature forest soil under Douglas fir plantations as an example of secondary succession in The Netherlands. As some of the experimental data were unreliable we compiled a set of various scenarios with different soil moisture regime, initial SOM pools and amount and quality of above and below ground litter input. This allowed us to find the scenarios that reflect the SOM dynamics more realistically. In the Scots pine forest, total litter input was estimated as 0.50 kg m⁻² year⁻¹. Two scenarios were defined for the test runs: (a) forest floor moisture regimes—‘dry, mesic and hydric’ and (b) augmenting a root litter pool with three ratios of needles and branches to roots: 1:1, 1:1.5 and 1:2.0. The scenario finally compiled had the following characteristics: (a) climate for dry site with summer drought and high winter moisture of forest floor; (b) a litter input of 0.25 kg m⁻² year⁻¹ above ground and 0.50 kg m⁻² year⁻¹ below ground; (c) a low nitrogen and ash content in all litter fall fractions. The test runs for the estimation of the initial SOM pools and the amount and proportion of above and below ground litter fall were also performed in the Douglas fir plantation. The inputs of above ground litter tested in various combinations were 0.30 and 0.60 kg m⁻² year⁻¹, and below ground litter 0.30, 0.60 and 0.90 kg m⁻² year⁻¹. The scenario that fitted the experimental data had an SOM pool of 20–25 kg m⁻², an aboveground litter input of 0.6 kg m⁻² year⁻¹and a below ground litter input of 0.9 kg m⁻² year⁻¹. The long-term simulation corresponded well with the observed patterns of soil organic matter accumulation associated with the forest soil development in primary and secondary succession. During primary succession in Scots pine forest on dry sand there is a consistent accumulation of a raw humus forest floor. The soil dynamics in the Douglas fir plantation also coincide with the observed patterns of SOM changes during the secondary succession, with SOM decreasing significantly under young forest, and SOM being restored in the older stands.     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.     

苏打盐碱土地区水田水盐运移模拟与预测研究

井灌种稻虽然是近年来松嫩平原西部土地苏打盐碱化治理的有效途径,但苏打盐碱地种稻改良对土壤盐分状况的长期影响,却缺乏定量化评价。作者选择该区代表性地点,通过田间观测与建立模型相结合的方法,对现有种植条件下的水田盐分动态变化进行了模拟和检验。研究发现,经过一定开垦年限土壤剖面平均含盐量下降到1.5g·kg-1左右后,在降水量正常的条件下,土壤盐分已基本达到平衡,不再随着开垦年限增加而进一步下降。相反,如遇干旱年份土壤盐分还会略有增加。需要进一步采取增加排水次数、改善土壤通透性等其它技术措施,才能使苏打盐碱地种稻改良达到更加理想的效果。     

推进GIS在环境规划中应用的探讨

本文在分析了地理信息系统引入环境规划带来的优越性的基础上，通过对目前我国环境领域中ＧＩＳ应用现状及实例研究，得出要深化ＧＩＳ在我国环境科学中的应用，就必须加强ＧＩＳ上的模型分析，并最终建立起ＧＩＳ模型库。     

Statistical inference on patch-specific survival and movement rates from marked animals

Empirical estimates of patch-specific survival and movement rates are needed to parametrize spatially explicit population models, and for inference on the effects of habitat quality and fragmentation on populations. Data from radio-marked animals, in which both the fates and habitat locations of animals are known over time, can be used in conjunction with continuous-time proportional hazards models to obtain inferences on survival rates. Discrete-time conditional logistic models may provide inference on both survival and movement rates. We use Monte Carlo simulation to investigate accuracy of estimates of survival from both approaches, and movement rates from conditional logistic regression, for two habitats. Bias was low (relative bias < 0.04) and interval coverage accurate (close to the nominal 0.95) for estimates of habitat effect on survival based on proportional hazards. Bias was high ( relative bias 0.60) and interval coverage poor ( = 0.26 vs. nominal 0.95) for estimates of habitat effect based on conditional logistic regression; bias was especially influenced by heterogeneity in survival and the shape of the hazard function, whereas both bias and coverage were affected by ‘memory’ effects in movement patterns. Bias estimates of movement rate was low ( relative bias < 0.05), but interval coverage was poor ( = 0.48–0.80), possibly as a result of poor performance of a Taylor series estimate of variance. An example is provided from a radio-telemetry study of 47 wintering American woodcock (Scolopax minor), illustrating practical difficulties in field studies to parametrize these models. We also discuss extensions of continuous-time models to explicitly include a movement process, and further examine tradeoffs between continuous and discrete models.