Information‐theoretic model selection affects home‐range estimation and habitat preference inference: a case study of male Reeves’s Pheasants Syrmaticus reevesii

Reeves’s Pheasant Syrmaticus reevesii is a vulnerable forest bird inhabiting broadleaved habitats dominated by oaks Quercus spp. in central China. Identifying home‐ranges and habitat associations is important for understanding the biology of this species and developing effective management and conservation plans. We used information‐theoretic criteria to evaluate the relative performance of four parametric (exponential power, one‐mode bivariate normal, two‐mode bivariate normal and two‐mode bivariate circle) and two non‐parametric models (adaptive and fixed kernel) for estimating home‐ranges and habitat associations of Reeves’s Pheasants. For parametric models, Akaike’s information criterion (AIC_c) and the likelihood cross‐validation criterion (CVC) were relatively consistent in ranking the bivariate exponential power model the least acceptable, whereas the two‐mode bivariate models performed better. The CVC suggested that kernel models, particularly the adaptive kernel, performed best among all six models evaluated. The average core area and 95% contour area based on the model with greatest support were 6.1 and 54.9 ha, respectively, and were larger than those estimated from other models. The discrepancy in estimates between models with highest and the lowest support decreased as the contour size increased; however, home‐range shapes differed between models. Minimum convex polygons that removed 5% of extreme data points (MCP95) were roughly half the size of home‐ranges based on kernel models. Estimates of home‐range and model evaluation were not affected by sample size (> 50 observations for each bird). Inference about habitat preference based on composition analysis and home‐range overlap varied between models. That with strongest support suggested that Reeves’s Pheasants selected mature fir and mixed forest, avoided farmland, and had mean among‐individual home‐range overlaps of 20%. We recommend non‐parametric methods, particularly the adaptive kernel method, for estimating home‐ranges and core areas for species with complex multi‐polar habitat preferences in heterogeneous environments with large habitat patches. However, we caution against the traditional convenience of using a single model to estimate home‐ranges and recommend exploration of multiple models for describing and understanding the ecological processes underlying space use and habitat associations.     

The species–area relationship in centipedes (Myriapoda: Chilopoda): a comparison between Mediterranean island groups

The present study article examines the shapes of centipede species–area relationships (SARs) in the Mediterranean islands, compares the results of the linear form of the power model between archipelagos, discusses biological significance of the power model parameters with other taxa on the Aegean archipelago, and tests for a significant small‐island effect (SIE). We used 11 models to test the SARs and we compared the quality‐of‐fit of all candidate models. The power function ranked first and Z‐values was in the range 0.106–0.334. We assessed the presence of SIEs by fitting both a continuous and discontinuous breakpoint regression model. The continuous breakpoint regression functions never performed much better than the closest discontinuous model as a predictor of centipede species richness. We suggest that the relatively low Z‐values in our data partly reflect better dispersal abilities in centipedes than in other soil invertebrate taxa. Longer periods of isolation and more recent island formation may explain the somewhat lower constant c in the western Mediterranean islands compared to the Aegean islands. Higher breakpoint values in the western Mediterranean may also be a result of larger distance to the mainland and longer separation times. Despite the differences in the geological history and the idiosyncratic features of the main island groups considered, the overall results are quite similar and this could be assigned to the ability of centipedes to disperse across isolation barriers. © 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 105 , 146–159.     

The use of CO2 flux time series for parameter and carbon stock estimation in carbon cycle research

Data assimilation (DA) is increasingly being employed to estimate the parameters and states of terrestrial ecosystem models from eddy covariance measurements of net carbon (C) fluxes. The length of the observation time series used varies for each study. The impact of these differences has not been quantified explicitly. Therefore, in this study, we investigate the importance of the time series length relative to observation noise and data gaps. Different length synthetic time series are used to determine the parameter and C stocks of a simple ecosystem C model. Two commonly used DA schemes are tested: the sequential Ensemble Kalman Filter (EnKF) and a batch Metropolis Markov chain Monte Carlo algorithm. Longer time series improve both the parameter and C pool estimates of the EnKF, while adversely affecting those of the Metropolis algorithm. For both DA approaches, the length of the time series has more influence on the parameter and pool estimates than the level of random noise or amount of data. In this study, the EnKF provides more robust parameter and C pool estimates than the Metropolis algorithm. Optimized parameters and states are often used as the basis for forecasting future responses. Despite having better parameter and C pool estimates, EnKF forecasts estimates have much larger uncertainties than the Metropolis algorithm forecast estimates. Finally, we suggest that the structure of simple box models, as used in this study, introduces a large degree of equifinality into DA. Neither DA scheme correctly accounts for the equifinality, but our results suggest that it is particularly problematic for the batch Metropolis algorithm.     

Modelling seasonal increments in size to determine the onset of annual growth in fishes

A new method based on modelling of seasonal growth increments ( G _SI) in total length was found useful for assessing the date of onset of annual growth for 16 fish species in a temperate fluvial lake. Model comparisons indicated that polynomial (linear or quadratic) functions provided better fits to seasonal growth and were more likely to avoid convergence problems than alternative non-linear models. There was little evidence for differences in the date of onset of growth between years, nor among age classes within individual species. The onset of growth also was to some extent synchronized among species and was concentrated within a narrow temporal window of c. 2 weeks, between 18 May and 2 June, which corresponded to mean water temperatures between 16·1 and 17·3° C. There was no apparent relationship between date of onset and species' thermal preferenda or thermal preferences. By producing a point estimate along with appropriate 95% CI, the G _SI method provides useful information on the onset of growth and the uncertainty about that estimate. The G _SI analyses can contribute to a better understanding of environmental influences on the onset of growth and the length of the growing season, and of thermal thresholds for growth, including their use in calculation of degree-day metrics.     

一种菊科植物抽提物对小鼠血液生化成分的影响

探讨一种菊科植物抽提物对小鼠血液生化成分的影响。利用半自动生化分析仪测定血液生化成分,结果表明该抽提物可降低血清白蛋白和总蛋白含量;降低乳酸脱氢酶、肌酸激酶活性和丙氧酸氧基转移酶活性;对血清中尿素、尿酸、肌酐都有明显降低作用;可减少血清二价金属离子Ca^2 、Mg^2 含量,增加Cl^-含量;对碱性磷酸酶、天冬氨酸氧基转移酶、血清无机磷影响不明显。     

The Effect of Branch Length Variation on the Selection of Models of Molecular Evolution

Models of sequence evolution play an important role in molecular evolutionary studies. The use of inappropriate models of evolution may bias the results of the analysis and lead to erroneous conclusions. Several procedures for selecting the best-fit model of evolution for the data at hand have been proposed, like the likelihood ratio test (LRT) and the Akaike (AIC) and Bayesian (BIC) information criteria. The relative performance of these model-selecting algorithms has not yet been studied under a range of different model trees. In this study, the influence of branch length variation upon model selection is characterized. This is done by simulating sequence alignments under a known model of nucleotide substitution, and recording how often this true model is recovered by different model-fitting strategies. Results of this study agree with previous simulations and suggest that model selection is reasonably accurate. However, different model selection methods showed distinct levels of accuracy. Some LRT approaches showed better performance than the AIC or BIC information criteria. Within the LRTs, model selection is affected by the complexity of the initial model selected for the comparisons, and only slightly by the order in which different parameters are added to the model. A specific hierarchy of LRTs, which starts from a simple model of evolution, performed overall better than other possible LRT hierarchies, or than the AIC or BIC. Received: 2 October 2000 / Accepted: 4 January 2001     

An asymptotic theory for model selection inference in general semiparametric problems

Hjort & Claeskens (2003) developed an asymptotic theoryfor model selection, model averaging and subsequent inferenceusing likelihood methods in parametric models, along with associatedconfidence statements. In this article, we consider a semiparametricversion of this problem, wherein the likelihood depends on parametersand an unknown function, and model selection/averaging is tobe applied to the parametric parts of the model. We show thatall the results of Hjort & Claeskens hold in the semiparametriccontext, if the Fisher information matrix for parametric modelsis replaced by the semiparametric information bound for semiparametricmodels, and if maximum likelihood estimators for parametricmodels are replaced by semiparametric efficient profile estimators.Our methods of proof employ Le Cam's contiguity lemmas, leadingto transparent results. The results also describe the behaviourof semiparametric model estimators when the parametric componentis misspecified, and also have implications for pointwise-consistentmodel selectors.     

Hierarchical Bayesian Estimation for the Number of Species

This paper is concerned with the estimation of the number of species in a population through a fully hierarchical Bayesian model using the Metropolis algorithm. The proposed Bayesian estimator is based on Poisson random variables with means that are distributed according to some prior distributions with unknown hyperparameters. An empirical Bayes approach is considered and compared with the fully Bayesian approach based on biological data.