应用能量利用特性对外来植物入侵潜力的判别分析研究

判别分析是多元统计分析中判断研究对象所属类型的一种重要方法.以华南地区5种主要入侵植物及其伴生或近缘的本地种为研究对象,测定了它们的生物量建成成本(construction cost,CC)和比叶面积(special leafarea,SLA),并以此作为判别因子尝试建立入侵种和本地种的判别模型.结果表明,入侵种和本地种之间的平均CC和SLA存在显著差异.以CC和SLA作为判别因子建立的判别模型进行回代检验,总的预测正确率为82.6%,判别效果较好.其中CC的判别函数系数较大,表明它在判别分析中起到了较为重要的作用,进一步证明了植物的能量利用特性是影响外来种入侵潜力的重要因素.利用此判别模型可以根据任何一种外来植物的CC和SLA来定量判断该物种入侵潜力的大小,为有效评价外来植物入侵潜力提供了新的定量方法;但由于所获得数据的不全面,该模型还存在不足,有待在今后的研究中进一步完善.     

A mechanistic model of GPS collar location data: Implications for analysis and bias mitigation

Global positioning system (GPS) collars have revolutionized the collection of animal location data; however, it is well-recognized that considerable bias can be present in these data due to habitat or behavior-induced obstruction of satellite signals resulting in inaccurate or missing locations. To date, no explicit theoretical framework of GPS fix acquisition specific to animal telemetry has been presented, and studies make differing assumptions regarding factors influencing GPS fix acquisition and how these data should be analyzed. Inappropriate statistical models have been used, interaction effects have been misunderstood, and the implementation of bias mitigation techniques has been problematic. Herein we outline current conceptual and analytical problems in the GPS animal telemetry literature, and subsequently present a theoretical model-based framework for GPS fix acquisition that clarifies the single and interactive effects of habitat and behavioral obstruction, fix interval, and collar model on GPS collar performance. By recognizing that GPS fix acquisition is a Bernoulli process, it becomes apparent that all forms of obstruction inherently interact with each other, making generalizations across study areas, study species, and collar models problematic. Stationary collar tests to determine the probability of fix acquisition (PFA), location accuracy, and the response to sources of obstruction are thus of limited applicability to animal-deployed collars. Bias mitigation techniques that extrapolate PFA models across samples, especially those using stationary collar tests to correct animal-deployed collars, are theoretically unsound. It is also demonstrated that nonlinearities in the relationships between sources of obstruction and PFA complicate PFA modeling with limited data and that even slight model misspecification can lead to considerable errors in correction factors, especially when using inverse weighting to mitigate bias. By emphasizing the importance of GPS collar sensitivity and ephemeris retention, the theoretical framework predicts that newer, more sensitive GPS collars will be less severely biased by sources of obstruction than reported for the older, less sensitive collars that have been used in the majority of GPS performance studies to date and we expect this trend to continue. This heuristic modeling exercise should be of value to researchers planning and analyzing studies using GPS collars and it also establishes a starting point for future theoretical investigations into GPS collar performance and bias mitigation.     

Cross-correlation bias in lag analysis of aquatic time series

Cross-correlation analysis is the most valuable and widely used statistical tool for evaluating the strength and direction of time-lagged relationships between ecological variables. Although it is well understood that temporal autocorrelation can inflate estimates of cross correlations and cause high rates of incorrectly concluding that lags exist among time series (i.e. type I error), in this study we show that a problem we term intra-multiplicity can cause substantial bias in cross-correlation analysis even in the absence of autocorrelation. Intra-multiplicity refers to the numerous time lags examined and cross-correlation coefficients computed within a pair of time series during cross-correlation analysis. We show using Monte Carlo simulations that intra-multiplicity can spuriously inflate estimates of cross correlations by identifying incorrect time lags. Further, unlike autocorrelation, which generally identifies lags close to the true lag, intra-multiplicity can erroneously identify lags anywhere in the time series and commonly results in a direction change of the correlation (i.e. positive or negative). Using Monte Carlo simulations we develop formulas that quantify the bias introduced by intra-multiplicity as a function of sample size, true cross correlation between the series, and the number of time lags examined. A priori these formulas enable researchers to determine the sample size needed to minimize the biases introduced by intra-multiplicity. A posteriori the formulas can be used to predict the expected bias and type I error rate associated with the data at hand, as well as the maximum number of time lags that can be analyzed to minimize the effects of intra-multiplicity. We examine the relationship between commercial catch of chum salmon and surface temperatures of the North Pacific (1925–1992) to illustrate the problems of intra-multiplicity in fisheries studies and the application of our formulas. These analyses provide a more robust framework to assess the temporal relationships between ecological variables. Received: 28 July 2000 / Accepted: 6 December 2000     

Geographic variation in Loligo forbesi in the Northeast Atlantic Ocean: analysis of morphometric data and tests of causal hypotheses

Geographic variation in the squid Loligo forbesi was investigated using multivariate analysis of morphometric and meristic characters in samples of squid taken from 13 localities in the Northeast Atlantic Ocean. Two character sets, body morphometrics and beak morphometrics, indicated similar patterns of variation, with squid from the Azores differing markedly from those on the continental shelf. No consistent pattern was apparent in meristic data. Partial Mantel tests indicated that similarity matrices for morphological data were significantly correlated with distance matrices for (a) geographic proximity, (b) whether the capture site was on the continental shelf or the Azorean bank, and (c) (beak data only) average seasurface temperature at site of capture. Partial Mantel tests on allozyme data for the same individuals support hypothesis (b). The results suggest that L. forbesi in the Azores may reasonably be regarded as a distinct stock, differing significantly from L. forbesi on the continental shelf.     

Sensitivity analysis on the ecological bias for Seoul tuberculosis data

In ecological studies, researchers often try to convey the analysis results to individual level based on aggregate data. In order to do this correctly, the possibility of ecological bias should be studied and addressed. One of the key ideas used to address the ecological bias issue is to derive the ecological model from the individual model and to check whether the parameter of interest in the individual model is identifiable in the ecological model. However, the procedure depends on unverifiable assumptions, and we recommend checking how sensitive the results are to these unverifiable assumptions. We analyzed the tuberculosis data that was collected in Seoul in 2005 using a spatial ecological regression model for the aggregate count data with spatial correlation, and found that the deprivation index is likely to have a small positive effect on the occurrence risk of tuberculosis in individual level in Seoul. We considered this finding in various aspects by performing in depth sensitivity analyses. In particular, our findings are shown to be robust to the distribution assumptions for the individual exposure and missing binary covariate across various scenarios.     

Multivariate morphometric analysis of two varieties of Littorina saxatilis from the Swedish west coast

Shell variation in Littorina saxatilis Olivi (=L. rudis) is examined with different techniques of multivariate analysis (canonical variate-, multiple discriminant-, principal component-, and principal coordinate analysis). In bivariate projections of the analyses, two populations, spatially only 60 m apart, but from different habitats, showed a phenotypic separation without overlap. One habitat is a boulder shore, while the other is a rocky cliff. A sample, geographically and environmentally intermediate to the exposed and sheltered populations, covered completely the morphologic gap between the two former groups, indicating a clinal variation in morphology over the environmental gradient exposed — sheltered. From this it is concluded that the two phenotypically distinct populations belong to the same species.     

Using discriminant analysis to determine the breaking criterion for an ISW propagating over a ridge

This study aims to develop methods that are capable of deciding the breaking criterion for an internal solitary wave (ISW) propagating over a submarine ridge. Laboratory experiments were conducted in a wave tank to measure ISWs propagating over a submarine ridge. The results suggest that the ISW-ridge interaction can be grouped according to three degrees of magnitude based on the blockage parameter ζ and the degree of blocking B. For classification reasons, we first present an alternative decision model for evaluating the interaction of ISWs with an underwater ridge in a two-layer system. This approach is based on a multivariate statistical method and discriminant analysis. Information obtained from the eigenvalues is used to combine different ratio measures which are defined according to every single input and output. The discriminant model effectively classifies units into distinct predefined groups. An experimental simulation is conducted to demonstrate the practical implementation of the ISW-ridge interaction. The results of the method applied in this example are statistically significant, demonstrating the effectiveness of the ISW-ridge interaction classification method.     

A Bayesian approach to retransformation bias in transformed regression

Ecological data analysis often involves fitting linear or nonlinear equations to data after transforming either the response variable, the right side of the equation, or both, so that the standard suite of regression assumptions are more closely met. However, inference is usually done in the natural metric and it is well known that retransforming back to the original metric provides a biased estimator for the mean of the response variable. For the normal linear model, fit under a log-transformation, correction factors are available to reduce this bias, but these factors may not be generally applicable to all model forms or other transformations. We demonstrate that this problem is handled in a straightforward manner using a Bayesian approach, which is general for linear and nonlinear models and other transformations and model error structures. The Bayesian framework provides a predictive distribution for the response variable so that inference can be made at the mean, or over the entire distribution to incorporate the predictive uncertainty.     

Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Bias originating from intrinsic nonlinearity in nonlinear models is caused by excess curvature in the solution locus of parameter estimates derived from least squares procedures. Bias due to intrinsic nonlinearity varies according to sample size as well as model specification. This paper analyses consequences of fractionising data into smaller sub-samples. Based on measurements of stem diameter and total tree height from the first Danish national forest inventory, it is demonstrated how data splitting at random may cause the intrinsic nonlinear curvature to exceed the critical F-value. Application of a Taylor-series expansion shows that, for all practical purposes, the bias in predictions of individual tree volume (based on stem diameter and tree height) is negligible. To minimize residual variance, intrinsic curvature and, in turn, prediction bias, it is recommended that data be stratified according to site conditions, stand characteristics or other relevant criteria. Finally, the preferred model should exhibit close-to-linear behaviour.     

Effect of different diets on the ultrastructure of hepatocytes of Chanos chanos fry (Chanidae: Teleostei): an electron microscopic and morphometric analysis

The hepatocytes of milkfish fry offered different artificial diets (carbohydrate-, lipid-, protein-oriented) and live food (Artemia spp., Brachionus plicatilis) differ considerably both qualitatively and quantitatively as was shown by means of transmission electron microscopy and planimeter. Food deprivation, too, resulted in ultrastructural alterations of milkfish fry hepatocytes. Thus, this cell type might be used as an indicator of quality and quantity of food in teleosts.Contribution No. 131 of the SEAFDEC Aquaculture Department     

Thalassia testudinum productivity: A field comparison of measurement methods

A 6-day in situ comparison between the Wetzel inorganic ¹⁴C uptake and Zieman leaf-biomass techniques for measuring net primary production rates in the seagrass Thalassia testudinum was performed in the northeastern Gulf of Mexico. Measurement differences between the two methods were insignificant when the ¹⁴C uptake technique was corrected for sediment ¹⁴C uptake (13%), incubation-chamber light-energy absorption (14%) and differences in total light-energy which resulted from the experimental design (7.7%). These results reinforce previous observations that the ¹⁴C technique estimates net particulate-carbon production.     

Approximate bias of the estimated proportion in group testing

The maximum likelihood estimator for estimating proportions by group testing is biased. An expression for the approximate bias has been previously presented, which enables the creation of a less biased estimator by removing the term of \(O(n^{-1})\). However, in this previous work the term of \(O(n^{-2})\) was incorrectly derived. This note gives a correct derivation, and examines the relative contribution of the two terms.     

Sand disposal of the painted ghost crab Ocypode gaudichaudii (Decapoda: Ocypodidae): A possible role in courtship

In 1991/1992, we studied the sand disposal behavior of the painted ghost crabs Ocypode gaudichaudii on the Pacific coast of Panamá. O. gaudichaudii either kick, dump or tamp sand they excavate from their burrows. Here we relate these three kinds of sand disposal to burrow structure and distribution, as well as to crab size and sex. Our objective was to determine whether tamping may be a male courtship signal. Burrows whose owners tamped sand were on average longer, deeper, and higher on the beach than were burrows whose owners kicked or dumped sand. Five burrow shapes were distinguished, with half-spiral and spiral shapes being most common among tamped burrows. All crabs excavated from tamped burrows were males. Tamped burrows peaked in abundance around full and new moons. These observations, together with what is known about mating and breeding behavior of other ghost crabs, suggest that tamping may be involved in O. gaudichaudii courtship.     

Prolonged withdrawal: A possible predator evasion behavior in Balanus glandula (Crustacea: Cirripedia)

The duration of cirral withdrawal in Balanus glandula (Darwin) varies by a factor of three depending on the type of stimulus applied. Contact with potential predators including thaidid gastropods (Thais emarginata, T. lamellosa), and forcipulate asteroids (Leptasterias hexactis, Pycnopodia helianthoides) elicits significantly longer withdrawal durations than contact with an herbivorous gastropod (Tegula pulligo), a grazing, spinulosid asteroid (Henricia leviuscula) or a neutral, brown algal stimulus (Fucus distichus). By substantially attenuating the release of metabolites, prolonged withdrawal probably increases the likelihood of being bypassed by nonvisual predators relying on chemical cues to verify that barnacles are alive. The reduced response to the non-predatory species indicates that this is not a generalized response to gastropods or asteroids, but rather that it appears to be specific to potential predatory species.     

Sensitivity of species-distribution models to error,bias, and model design: An application to resource selection functions for woodland caribou

Models that predict distribution are now widely used to understand the patterns and processes of plant and animal occurrence as well as to guide conservation and management of rare or threatened species. Application of these methods has led to corresponding studies evaluating the sensitivity of model performance to requisite data and other factors that may lead to imprecise or false inferences. We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability. We used a one-at-a-time sample design and GPS location data for woodland caribou (Rangifer tarandus caribou) to assess one common species-distribution model: a resource selection function. Our measures of sensitivity included change in coefficient values, prediction success, and the area of mapped habitats following the systematic introduction of geographic error and bias in occurrence data, thematic misclassification of resource maps, and variation in model design. Results suggested that error, bias and model variation have a large impact on the direct interpretation of coefficients. Prediction success and definition of important habitats were less responsive to the perturbations we introduced to the baseline model. Model coefficients, prediction success, and area of ranked habitats were most sensitive to positional error in species locations followed by sampling bias, misclassification of resources, and variation in model design. We recommend that researchers report, and practitioners consider, levels of error and bias introduced to predictive species-distribution models. Formal sensitivity and uncertainty analyses are the most effective means for evaluating and focusing improvements on input data and considering the range of values possible from imperfect models.     

Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird

We assessed the occurrence of a common river bird, the Plumbeous Redstart Rhyacornis fuliginosus, along 180 independent streams in the Indian and Nepali Himalaya. We then compared the performance of multiple discrimant analysis (MDA), logistic regression (LR) and artificial neural networks (ANN) in predicting this species’ presence or absence from 32 variables describing stream altitude, slope, habitat structure, chemistry and invertebrate abundance. Using the entire data (=training set) and a threshold for accepting presence in ANN and LR set to P≥0.5, ANN correctly classified marginally more cases (88%) than either LR (83%) or MDA (84%). Model performance was assessed from two methods of data partitioning. In a ‘leave-one-out’ approach, LR correctly predicted more cases (82%) than MDA (73%) or ANN (69%). However, in a holdout procedure, all the methods performed similarly (73–75%). All methods predicted true absence (i.e. specificity in holdout: 81–85%) better than true presence (i.e. sensitivity: 57–60%). These effects reflect species’ prevalence (=frequency of occurrence), but are seldom considered in distribution modelling. Despite occurring at only 36% of the sites, Plumbeous Redstarts are one of the most common Himalayan river birds, and problems will be greater with less common species. Both LR and ANN require an arbitrary threshold probability (often P=0.5) at which to accept species presence from model prediction. Simulations involving varied prevalence revealed that LR was particularly sensitive to threshold effects. ROC plots (received operating characteristic) were therefore used to compare model performance on test data at a range of thresholds; LR always outperformed ANN. This case study supports the need to test species’ distribution models with independent data, and to use a range of criteria in assessing model performance. ANN do not yet have major advantages over conventional multivariate methods for assessing bird distributions. LR and MDA were both more efficient in the use of computer time than ANN, and also more straightforward in providing testable hypotheses about environmental effects on occurrence. However, LR was apparently subject to chance significant effects from explanatory variables, emphasising the well-known risks of models based purely on correlative data.     

Molecular and morphometric variation in European populations of the articulate brachiopod Terebratulina retusa

Molecular and morphometric variation within and between population samples of the articulate brachiopod Terebratulina spp., collected in 1985–1987 from a Norwegian fjord, sea-lochs and coastal sites in western Scotland, the southern English Channel (Brittany) and the western Mediterranean, were measured by the analysis of variation in the lengths of mitochondrial DNA (mtDNA) fragments produced by digestion with nine restriction endonucleases and by multivariate statistical analysis of six selected morphometric parameters. Nucleotide difference within each population sample was high. Nucleotide difference between population samples from the Scottish sites, both those that are tidally contiguous and those that appear to be geographically isolated, were not significantly different from zero. Nucleotide differences between the population samples from Norway, Brittany, Scotland and the western Mediterranean were also very low. Morphometric analysis confirmed the absence of substantial differentiation.