Modeling high-frequency position data of large herbivores with a phase-state model

Understanding the rules and factors that drive the foraging behavior of large herbivores is important to describe their interaction with the landscape at various spatial scales. Some unresolved questions refer to landscape-behavioral interactions that result in oriented or random search in seasonally changing landscapes. Remotely sensed position data indicate that herbivores select local patches of heterogeneous landscapes depending on a complex host of dynamically varying animal and environmental conditions. Since foraging paths consist in successions of relatively short steps, increasing the frequency at which position information is acquired would contribute to entangle the mechanisms resulting in herbivores’ foraging paths. We addressed the question whether herbivores would obtain information at a patch scale that would modify their distribution at a landscape scale based on directed movement or navigation ability. We considered a set of 100,000 high-frequency (1 min intervals) position data of several free-ranging sheep (Ovis aries) at a seasonal-varying range (Patagonian Monte, Argentina) and observed their movements at landscape and at single vegetation patch scales. At a landscape scale, we inspected the spatial co-variation of seasonally varying forage offer and ewes’ movement speeds. At a patch scale, we developed a phase-state (P-S) model of movement cycles based on the occurrence of behavioral phases along foraging paths, and fitted it to the observed daily time series of ewes’ movement speeds. Ewes were preferentially distributed in areas with high forage offer during periods of low forage availability and the reverse occurred during the season of high forage availability. Parameters of the model of activity cycles amenable to control by ewes (duration of speed phases, time elapsed between speed cycles) did not covariate with forage offer, but varied significantly among ewes. The shape (kurtosis) parameter of the model of movement cycles, one which is unlikely under ewes’ control, co-varied significantly with spatial forage offer but did not differ among ewes. We conclude that ewes allocated foraging time along a series of similar movement efforts irrespective of forage availability at small patches. Average forage scarcity at multi-patch level increases the ratio of searching to feeding time. This results in apparent selective time allocation to richer forage areas but does not imply evidence for oriented movement at a landscape scale. We advance a behavioral-based definition of forage patches and discuss its implications in developing foraging theory and models. The P-S model applied to high-frequency position data of large herbivores substantially improves the interpretation of the factors controlling their time allocation in space with respect to previous models of herbivore spatial behavior by discriminating among behavioral-based and environmentally induced components of their movements.     

Regression analysis of forest inventory data with time and space dependencies

In this paper the data of a forest health inventory are analyzed. Since 1983 the degree of defoliation, together with various explanatory variables (covariates) concerning stand, site, soil and weather, are recorded by the second of the two authors, in the forest district of Rothenbuch (Spessart, Bavaria). The focus is on the space and time dependencies of the data. The mutual relationship of space-time functions and the set of covariates is evaluated. For this we use generalized linear models (GLMs) for ordinal response variables and semiparametric estimation approaches. By using goodness-of-fit measures it turns out that (i) the contribution of space-time functions is quantitatively comparable with that of the set of covariates, (ii) the contribution of space-time functions is small compared with the contribution of a set of variables describing the last-year and neighboring response values. By applying appropriate residual methods a detailed analysis of the individual sites in the area can be carried out. This analysis reveals where the predictive power of the covariates fail to explain the observed defoliation.     

Intercomparison of Two Models,ETA and RAMS,with TRACT Field Campaign Data

In this work a model intercomparison between RAMS and ETA models is carried out, with the aim of evaluating the quality and accuracy of these mesoscale models in reproducing the time evolution of the meteorology in real complex terrain. This is of great importance not only for meteorological forecast but also for air quality assessment. Numerical simulations are performed to reproduce the mean variables' fields and to compare them with measurements collected during the field campaign TRACT. The domain covers the Rhine valley and surrounding mountainous region and we consider a time period of two days. Results from simulations are compared to observations relative to ground stations and radiosoundings. A qualitative analysis is joined to a quantitative estimation of some reference statistical indexes. Both RAMS and ETA models performances are satisfactory when compared to the measured data and also their relative agreement is good. The mean variable fields are reproduced with a satisfactory degree of reliability, even if the simulated profiles are not able to describe the largest fluctuations of the variables. At the surface stations, the best agreement between predictions and observations is obtained for the wind velocity, while the quality of the results is lower for temperature and humidity.     

Parameterizing,evaluating and comparing metapopulation models with data from individual-based simulations

Due to the lack of sufficient data and appropriate ecological information parameterizing predictive population dynamical models usually is a difficult task. The approach proposed in this study is meant to overcome this problem by using detailed individual-based simulations to generate artificial data. With short-term data samples, the models to be investigated can be parameterized and their predictions be compared. The flexibility of individual-based simulations as experimental tools also facilitates the evaluation and comparison of different (aggregated) model types. The presented approach is a step towards unifying models of different complexity. As an example we applied it to two metapopulation models of insect species in a highly fragmented landscape: the well-known incidence function model with a patch-based representation of space and a grid-based analogue. The models are tested with respect to their data requirement and recommendations for a better data sampling are derived.     

On interacting bee/mite populations: a stochastic model with analysis using cumulant truncation

Parasitism by the Varroa mite has had recent drastic impact on both managed and feral bee colonies. This paper proposes a stochastic population dynamics model for interacting African bee colony and Varroa mite populations. Cumulant truncation procedures are used to obtain approximate transient cumulant functions, unconstrained by the usual assumption of bivariate Normality, for an assumed large-scale model. The apparent size of the variance and skewness functions suggest the importance of the proposed truncation procedure which retains some higher-order cumulants, but determining the accuracy of the approximations is problematical. A smaller-scale bee/Varroa mite model is hence proposed and investigated. The accuracy for the means is exceptional, for the second-order cumulants is moderate, and for some third-order cumulants is poor. Notwithstanding the poor accuracy of a skewness approximation, the saddlepoint approximations for the marginal transient population size distributions are excellent. The cumulant truncation methodology is very general, and research is continuing in its application to this new class of host-parasite models.     

Bayesian Ecological Regression with Latent Factors: Atmospheric Pollutants Emissions and Mortality for Lung Cancer

In this work we propose a Bayesian ecological analysis in which a latent variable summarizes data on emissions of atmospheric pollutants. We specified a hierarchical Bayesian model with spatially structured and unstructured random terms with a nested latent factor model. This can be considered a combination of the convolution spatial model of Besag et al. (1991) and an ecological regression analysis in which a latent variable plays the role of the covariate. The unified approach allows to proper account for the uncertainty in the latent score estimation in the regression analysis. The Bayesian Latent Factor model is used to summarize the information on environmental pressure derived from three stressors: Carbon Monoxide, Nitrogen Oxides and Inhalable Particles. We found evidence of positive correlation between Lung cancer mortality and environmental pressure indicators, in males, Tuscany (Italy), 1995–1999. Environmental pressure seems to be restricted to fourteen municipalities (top 5% of the Latent Factor distribution). The model identified two areas with high point source emissions.     

Comparing model averaging with other model selection strategies for benchmark dose estimation

Model averaging (MA) has been proposed as a method of accommodating model uncertainty when estimating risk. Although the use of MA is inherently appealing, little is known about its performance using general modeling conditions. We investigate the use of MA for estimating excess risk using a Monte Carlo simulation. Dichotomous response data are simulated under various assumed underlying dose–response curves, and nine dose–response models (from the USEPA Benchmark dose model suite) are fit to obtain both model specific and MA risk estimates. The benchmark dose estimates (BMDs) from the MA method, as well as estimates from other commonly selected models, e.g., best fitting model or the model resulting in the smallest BMD, are compared to the true benchmark dose value to better understand both bias and coverage behavior in the estimation procedure. The MA method has a small bias when estimating the BMD that is similar to the bias of BMD estimates derived from the assumed model. Further, when a broader range of models are included in the family of models considered in the MA process, the lower bound estimate provided coverage close to the nominal level, which is superior to the other strategies considered. This approach provides an alternative method for risk managers to estimate risk while incorporating model uncertainty.

Complex dynamics of the population with a simple age structure

The effects of the following modes of density-dependent control of population growth: density-dependent birth rate, adult survival rate, juvenile survival rate are compared based on the mathematical model of population dynamics. It is shown that the most efficient mechanisms limiting population size are decreasing with the growth of the adult population birth rate and/or the decreasing survival rate of the offspring with the increase in their number. However, these same mechanisms are responsible for oscillations of the population size and its chaotic change. The density-dependence of the adult survival rate is not efficient in constraining the population growth, but it can substantially limit the magnitude of oscillations of the population size.     

Metropolitan Open-Space Protection with Uncertain Site Availability

Abstract:  Urban planners acquire open space to protect natural areas and provide public access to recreation opportunities. Because of limited budgets and dynamic land markets, acquisitions take place sequentially depending on available funds and sites. To address these planning features, we formulated a two-period site selection model with two objectives: maximize the expected number of species represented in protected sites and maximize the expected number of people with access to protected sites. These objectives were both maximized subject to an upper bound on area protected over two periods. The trade-off between species representation and public access was generated by the weighting method of multiobjective programming. Uncertainty was represented with a set of probabilistic scenarios of site availability in a linear-integer formulation. We used data for 27 rare species in 31 candidate sites in western Lake County, near the city of Chicago, to illustrate the model. Each trade-off curve had a concave shape in which species representation dropped at an increasing rate as public accessibility increased, with the trade-off being smaller at higher levels of the area budget. Several sites were included in optimal solutions regardless of objective function weights, and these core sites had high species richness and public access per unit area. The area protected in period one depended on current site availability and on the probabilities of sites being undeveloped and available in the second period. Although the numerical results are specific for our study, the methodology is general and applicable elsewhere.     

Modelling heterogeneity in the recoveries of marked animal populations with covariates of individual animals, groups of animals or recovery time

A general framework is developed for modelling rates of survival and recovery of marked animal populations in terms of auxiliary information collected at the time of marking. The framework may be used to estimate differences in survival or recovery among individual animals, groups of animals, and recovery times. Analyses of the recoveries of tagged fish and banded bird populations are used to illustrate the specification and selection of various models.     

Selective reduction of NO by photo-SCR with ammonia in an annular fixed-film photoreactor

Gaseous NO was photocatalytically reduced at room temperature by photo-assisted selective catalytic reduction (photo-SCR) with ammonia over TiO₂ in this study. NO reduction efficiency and N₂ selectivity were determined from gases composition at the outlet stream of photoreactor. Effect of operating conditions, e.g. light intensity and inlet concentrations of ammonia and oxygen, on the NO reduction efficiency and N₂ selectivity were discussed to determine the feasible operating condition for photocatalytic reduction of NO. Experimental results showed that selective catalytic reduction of NO with ammonia over TiO₂ in the presence of oxygen was a spontaneous reaction in dark. The photoirradiation on the TiO₂ surface caused remarkable photocatalytic reduction of NO to form N₂, NO₂, and N₂O under 254 nm UV illuminations, while almost 90% of N₂ selectivity was achieved in this study. The ammonia and oxygen molecules played the roles of reductant and oxidant for NO reduction and active sites regeneration, respectively. The reduction of NO was found to be increased with the increase of inlet ammonia and oxygen concentrations until specific concentrations because of the limited active sites on the surface of TiO₂. The kinetic model proposed in this study can be used to reasonably describe the reaction mechanism of photo-SCR.     

基于ARIMA模型的区域水生态足迹时间序列分析

20世纪末以来,生态足迹理论应用研究成为生态经济学的一个热点,而生态足迹指标的可预测性一直是研究中争论的一个焦点。通过分析1949—1998年广州市的水生态足迹变化,并对计算结果进行ARIMA模型时间序列预测分析,以揭示广州水资源利用和水产品消耗的发展变化规律,并根据其发展变化规律预测广州市未来水生态足迹的变动。结果表明,ARIMA(2,2,2)模型能较好的拟合广州市1949—1993年期间水生态足迹变化,经1994—1998年实际计算结果验证所建模型,误差在5%左右;应用ARIMA(2,2,2)预测广州市1999—2008年的人均水生态足迹,结果分别为:0.5935,0.656 2,0.714 7,0.779 0,0.840 2,0.901 55,0.964 11,1.025 57,1.087 44,1.149 43 hm2,预测结果表明广州市1999—2008年期间的人均水生态足迹仍呈较快上升趋势,居民对于水产品的需求提高以及城市发展、人口增加等社会经济因素是导致这种趋势产生的主要原因。     

Modelling skewed data with many zeros: A simple approach combining ordinary and logistic regression

We discuss a method for analyzing data that are positively skewed and contain a substantial proportion of zeros. Such data commonly arise in ecological applications, when the focus is on the abundance of a species. The form of the distribution is then due to the patchy nature of the environment and/or the inherent heterogeneity of the species. The method can be used whenever we wish to model the data as a response variable in terms of one or more explanatory variables. The analysis consists of three stages. The first involves creating two sets of data from the original: one shows whether or not the species is present; the other indicates the logarithm of the abundance when it is present. These are referred to as the presence data and the log-abundance data, respectively. The second stage involves modelling the presence data using logistic regression, and separately modelling the log-abundance data using ordinary regression. Finally, the third stage involves combining the two models in order to estimate the expected abundance for a specific set of values of the explanatory variables. A common approach to analyzing this sort of data is to use a ln (y+c) transformation, where c is some constant (usually one). The method we use here avoids the need for an arbitrary choice of the value of c, and allows the modelling to be carried out in a natural and straightforward manner, using well-known regression techniques. The approach we put forward is not original, having been used in both conservation biology and fisheries. Our objectives in this paper are to (a) promote the application of this approach in a wide range of settings and (b) suggest that parametric bootstrapping be used to provide confidence limits for the estimate of expected abundance.     

Maximum likelihood method for parameter estimation in non-linear models with below detection data

The maximum likelihood (ML) method for regression analyzes of censored data (below detection limit) for nonlinear models is presented. The proposed ML method has been translated into an equivalent least squares method (ML-LS). A two stage iterative algorithm is proposed to estimate statistical parameters from the derived least squares translation. The developed algorithm is applied to a nonlinear model for prediction of ambient air CO concentration in terms of concentrations of respirable particulate matter (RSPM) and NO₂. It has been shown that if censored data are ignored or estimated through simplifications such as (i) censored data are equal to detection limit, (ii) censored data are half of the difference between detection limit and lower limit (e.g., zero or background level) or (iii) censored data are equal to lower limit, this can cause significant bias in estimated parameters. The developed ML-LS method provided better estimates of parameters than any of the simplifications in censored data.     

Robust trend tests with application to toxicology

In most real data situations in the one-way design both the underlying distribution and the shape of the dose-response curve are a priori unknown. The power of a trend test strongly depends on both. However, tests which are routinely used to analyze toxicological assays must be robust. We use nonparametric tests with different scores—powerful for different distributions—and different contrasts—powerful for different shapes—and use the maximum of all test statistics as a new test statistic. Simulation results indicate that this maximum test, which is a nonparametric multiple contrast test, stabilizes the power for various shapes and distributions. The investigated tests are applied to the data of a toxicological assay.     

Deterministic approach to the study of the interaction predator-prey in a chemostat with predator mutual interference. Implications for the paradox of enrichment

Our understanding of predator-prey systems has progressed in recent decades mainly due to the ability to test models in chemostats. This study aimed to develop a deterministic model using differential equations to reproduce the dynamics of the interaction of a predator and a prey in a two stage chemostat focusing in the proposed previous prey dependent model of Fussmann et al. (2000) [Fussmann, G.F., Ellner, S.P., Shertzer, K.W., Hairston Jr., N.G., 2000. Crossing the Hopf bifurcation in a live predator-prey system. Science 290, 1358-1360]. The main problem with that model, but parameterized with the values obtained in this study (particularly the concentration of nutrient), was that the temporal trajectory of both the prey and the predator showed very high peaks that eventually led to the extinction of predator in all cases. In the same way the experimental time series obtained in this study does not exhibit the behavior predicted by the model of Fussman et al. On the contrary, as prey density increases, the system actually becomes more stable. Finally, the model that best explained the behavior of the predator and prey in the chemostat, at medium to high dilution rates, was the ratio dependent (algae-nitrogen) model with mutual interference measured in the chemostat (rotifer-alga) and that incorporated the age structure of the predator. Qualitative analysis of the dynamic behavior enabled evaluation of coexistence at equilibrium, coexistence on limit cycles, extinction of the predator or extinction of both populations.     

A General Model for Analyzing Data from Mark-recapture Experiments with an Application to the Pacific Halibut

A general model is developed to examine the patterns of the regional movement of tagged and released fish from mark-recapture experiments. It is a stochastic model that incorporates fishing mortality, natural mortality, fish movement, tag-shedding, and different rates of reporting. A likelihood function is constructed for estimating its parameters. We used this model to analyze data on the Pacific halibut from mark-recapture experiments conducted by the International Pacific Halibut Commission (IPHC), with a total of 36,058 releases from 1982 to 1986 and 5,826 recoveries from 1982 to 2000. We estimated their rates of movement among IPHC management areas, along with their instantaneous rates of natural and fishing mortalities. Our analysis revealed that fish movement was not significant among areas, with a resident probability of > 0.92. This lends support to the IPHC catch-at-age stock assessment model (which has no built-in movement components). The estimated instantaneous rate of natural mortality (0.198 year⁻¹) lies between that assumed in all IPHC stock assessments before 1998 (0.20 year⁻¹) and that from 1999 onwards (0.15 year⁻¹). The estimates of the instantaneous rates of fishing mortality were consistent with those from the IPHC stock assessment model. Received: April 2003 / Revised: May 2005     

Quantifying Habitat Requirements of Tree-Living Species in Fragmented Boreal Forests with Bayesian Methods

Abstract:  Quantitative conservation objectives require detailed consideration of the habitat requirements of target species. Tree-living bryophytes, lichens, and fungi are a critical and declining biodiversity component of boreal forests. To understand their requirements, Bayesian methods were used to analyze the relationships between the occurrence of individual species and habitat factors at the tree and the stand scale in a naturally fragmented boreal forest landscape. The importance of unexplained between-stand variation in occurrence of species was estimated, and the ability of derived models to predict species' occurrence was tested. The occurrence of species was affected by quality of individual trees. Furthermore, the relationships between occurrence of species at the tree level and size and shape of stands indicated edge effects, implying that some species were restricted to interior habitats of large, regular stands. Yet for the habitat factors studied, requirements of many species appeared similar. Species occurrence also varied between stands; most of the seemingly suitable trees in some stands were unoccupied. The models captured most variation in species occurrence at tree level. They also successfully accounted for between-stand variation in species occurrence, thus providing realistic simulations of stand-level occupancy of species. Important unexplained between-stand variation in species occurrence warns against a simplified view that only local habitat factors influence species' occurrence. Apparently, similar stands will host populations of different sizes due to historical, spatial, and stochastic factors. Thus, habitat suitability cannot be assessed simply by population sizes, and stands lacking a species may still provide suitable habitat and merit protection.     

应用CGE模型量化分析中国实施能源环境税政策的可行性

应用一个可计算一般均衡(CGE)模型对中国若干工业行业按照差别税率对所投入的煤炭和油气产品征收从价能源环境税的情况进行了量化模拟,预测了该项政策在中国实施的可行性;并在量化模拟和分析之后得出如下结论:即征收能源环境税有助于促进工业部门,尤其是能源密集型行业,节能、降耗、改善能源消费结构和削减SO2 和CO2 污染排放,但该项政策的实施并不能够从根本上促进能源利用效率的提高,反而易对宏观经济造成负面影响,因此在中国目前现阶段不宜作为主要能源环境政策备选方案