Can entropy maximization use functional traits to explain species abundances? A comprehensive evaluation

Entropy maximization (EM) is a method that can link functional traits and community composition by predicting relative abundances of each species in a community using limited trait information. We developed a complementary suite of tests to examine the strengths and limitations of EM and the community-aggregated traits (CATs; i.e., weighted averages) on which it depends that can be applied to virtually any plant community data set. We show that suites of CATs can be used to differentiate communities and that EM can address the classic problem of characterizing ecological niches by quantifying constraints (CATs) on complex trait relationships in local communities. EM outperformed null models and comparable regression models in communities with different levels of dominance, diversity, and trait similarity. EM predicted well the abundance of the dominant species that drive community-level traits; it typically identified rarer species as such, although it struggled to predict the abundances of the rarest species in some cases. Predictions were sensitive to choice of traits, were substantially improved by using informative priors based on null models, and were robust to variation in trait measurement due to intraspecific variability or measurement error. We demonstrate how similarity in species' traits confounds predictions and provide guidelines for applying EM.     

Environmental filtering and land-use history drive patterns in biomass accumulation in a mediterranean-type landscape

Aboveground biomass (AGB) reflects multiple and often undetermined ecological and land-use processes, yet detailed landscape-level studies of AGB are uncommon due to the difficulty in making consistent measurements at ecologically relevant scales. Working in a protected mediterranean-type landscape (Jasper Ridge Biological Preserve, California, USA), we combined field measurements with remotely sensed data from the Carnegie Airborne Observatory's light detection and ranging (lidar) system to create a detailed AGB map. We then developed a predictive model using a maximum of 56 explanatory variables derived from geologic and historic-ownership maps, a digital elevation model, and geographic coordinates to evaluate possible controls over currently observed AGB patterns. We tested both ordinary least-squares regression (OLS) and autoregressive approaches. OLS explained 44% of the variation in AGB, and simultaneous autoregression with a 100-m neighborhood improved the fit to an r2 = 0.72, while reducing the number of significant predictor variables from 27 variables in the OLS model to 11 variables in the autoregressive model. We also compared the results from these approaches to a more typical field-derived data set; we randomly sampled 5% of the data 1000 times and used the same OLS approach each time. Environmental filters including incident solar radiation, substrate type, and topographic position were significant predictors of AGB in all models. Past ownership was a minor but significant predictor, despite the long history of conservation at the site. The weak predictive power of these environmental variables, and the significant improvement when spatial autocorrelation was incorporated, highlight the importance of land-use history, disturbance regime, and population dynamics as controllers of AGB.     

Latitudinal gradients in species richness for South American Mytilidae and Ostreidae: can alternative hypotheses be evaluated by a correlative approach?

We tested to what extent mean sea surface temperature, geometric constraints in range size frequency distributions (the mid-domain effect) and geographical coastline distance to the equator are related to species richness of coastal Mytilidae and Ostreidae in the Pacific and Atlantic coasts of South America (excluding islands). The location and magnitude of the peaks in species richness, as well as the shape of the pattern, varied between oceans. Results were not biased by spatial autocorrelation, although strong multicollinearity among predictor variables was detected. However, these regional-extent regression models suggest differences in the causal factors that explain richness gradients of studied bivalves in South American coasts, most likely related to historical events such as the Southeastern Pacific Pleistocene mass extinction of bivalves. Our results reinforced the conclusion that there is no single best explanatory cause for the latitudinal gradient in species richness and showed that the correlative approach is not useful when predictor variables are strongly correlated.     

Spatial residual analysis of six modeling techniques

In recent years alternative modeling techniques have been used to account for spatial autocorrelations among data observations. They include linear mixed model (LMM), generalized additive model (GAM), multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network, and geographically weighted regression (GWR). Previous studies show these models are robust to the violation of model assumptions and flexible to nonlinear relationships among variables. However, many of them are non-spatial in nature. In this study, we utilize a local spatial analysis method (i.e., local Moran coefficient) to investigate spatial distribution and heterogeneity in model residuals from those modeling techniques with ordinary least-squares (OLS) as the benchmark. The regression model used in this study has tree crown area as the response variable, and tree diameter and the coordinates of tree locations as the predictor variables. The results indicate that LMM, GAM, MLP and RBF may improve model fitting to the data and provide better predictions for the response variable, but they generate spatial patterns for model residuals similar to OLS. The OLS, LMM, GAM, MLP and RBF models yield more residual clusters of similar values, indicating that trees in some sub-areas are either all underestimated or all overestimated for the response variable. In contrast, GWR estimates model coefficients at each location in the study area, and produces more accurate predictions for the response variable. Furthermore, the residuals of the GWR model have more desirable spatial distributions than the ones derived from the OLS, LMM, GAM, MLP and RBF models.     

Semi-nonparametric Distribution-Free Dichotomous Choice Contingent Valuation

We apply a semi-nonparametric distribution-free estimator for binary discrete response data to the estimation of a dichotomous choice contingent valuation model. Using this estimator, mean and median compensating and equivalent variation can be consistently estimated without making nontheoretically motivated assumptions on consumer' preferences. The approach is illustrated using a contingent valuation survey of willingness to pay for reduction of risk of premature death due to exposure to hazardous waste. We find that a conventional parametric estimator and the proposed estimator give similar estimates of unconditional WTP, but that conditional on explanatory variables the estimates are quite different.     

Fourth-corner correlation is a score test statistic in a log-linear trait–environment model that is useful in permutation testing

Ecologists wish to understand the role of traits of species in determining where each species occurs in the environment. For this, they wish to detect associations between species traits and environmental variables from three data tables, species count data from sites with associated environmental data and species trait data from data bases. These three tables leave a missing part, the fourth-corner. The fourth-corner correlations between quantitative traits and environmental variables, heuristically proposed 20 years ago, fill this corner. Generalized linear (mixed) models have been proposed more recently as a model-based alternative. This paper shows that the squared fourth-corner correlation times the total count is precisely the score test statistic for testing the linear-by-linear interaction in a Poisson log-linear model that also contains species and sites as main effects. For multiple traits and environmental variables, the score test statistic is proportional to the total inertia of a doubly constrained correspondence analysis. When the count data are over-dispersed compared to the Poisson or when there are other deviations from the model such as unobserved traits or environmental variables that interact with the observed ones, the score test statistic does not have the usual chi-square distribution. For these types of deviations, row- and column-based permutation methods (and their sequential combination) are proposed to control the type I error without undue loss of power (unless no deviation is present), as illustrated in a small simulation study. The issues for valid statistical testing are illustrated using the well-known Dutch Dune Meadow data set.     

Sex linkage among genes controlling sexually selected traits

Using literature data on reciprocal crosses I estimated the influence of sex-chromosomal genes on morphological and behavioral traits. To determine a special role of the sex chromosomes for sexually selected traits, I compared the estimated influence of X-chromosomal genes on sexually selected traits with their estimated influence on traits not under sexual selection. About one-third of the phenotypic variation in sexually selected traits is caused by X-chromosomal genes. There was, in contrast, no significant influence of X-chromosomal genes on traits that were classified as not sexually selected. Sexually selected traits thus seem to be influenced significantly more by X-chromosomal genes than traits not under sexual selection. Though this differential influence of X-chromosomal genes cannot readily be explained with current theoretical knowledge, it may shed some light on X-linked hybrid sterility and on the discussion between arbitrary and good-gene models for the evolution of female choice. Received: 30 January 1998 / Accepted after revision: 20 June 1998     

Heterozygosity‐Fitness Correlations and Inbreeding Depression in Two Critically Endangered Mammals

The relation among inbreeding, heterozygosity, and fitness has been studied primarily among outbred populations, and little is known about these phenomena in endangered populations. Most researchers conclude that the relation between coefficient of inbreeding estimated from pedigrees and fitness traits (inbreeding‐fitness correlations) better reflects inbreeding depression than the relation between marker heterozygosity and fitness traits (heterozygosity‐fitness correlations). However, it has been suggested recently that heterozygosity‐fitness correlations should only be expected when inbreeding generates extensive identity disequilibrium (correlations in heterozygosity and homozygosity across loci throughout the genome). We tested this hypothesis in Mohor gazelle (Gazella dama mhorr) and Iberian lynx (Lynx pardinus). For Mohor gazelle, we calculated the inbreeding coefficient and measured heterozygosity at 17 microsatellite loci. For Iberian lynx, we measured heterozygosity at 36 microsatellite loci. In both species we estimated semen quality, a phenotypic trait directly related to fitness that is controlled by many loci and is affected by inbreeding depression. Both species showed evidence of extensive identity disequilibrium, and in both species heterozygosity was associated with semen quality. In the Iberian lynx the low proportion of normal sperm associated with low levels of heterozygosity was so extreme that it is likely to limit the fertility of males. In Mohor gazelle, although heterozygosity was associated with semen quality, inbreeding coefficient was not. This result suggests that when coefficient of inbreeding is calculated on the basis of a genealogy that begins after a long history of inbreeding, the coefficient of inbreeding fails to capture previous demographic information because it is a poor estimator of accumulated individual inbreeding. We conclude that among highly endangered species with extensive identity disequilibrium, examination of heterozygosity‐fitness correlations may be an effective way to detect inbreeding depression, whereas inbreeding‐fitness correlations may be poor indicators of inbreeding depression if the pedigree does not accurately reflect the history of inbreeding. Correlaciones Heterocigosidad‐ Adaptabilidad y Depresión Endogámica en Dos Especies de Mamíferos Críticamente en Peligro     

Ecological Determinants of Distribution Decline and Risk of Extinction in Moths

Abstract:  For successful conservation of species it is important to identify traits that predispose species to the risk of extinction. By identifying such traits conservation efforts can be directed toward species that are most at risk of becoming threatened. We used data derived from the literature to determine ecological traits that affect distribution, distribution change, and the risk of extinction in Finnish noctuid moths (Lepidoptera, Noctuidae). The ecological traits we examined included body size, larval specificity, length of the flight period, and overwintering stage. In addition, in monophagous species we examined the effects of resource distribution. Larval specificity, length of the flight period, and the overwintering stage each had an independent effect on the risk of extinction when the effects of other traits were controlled by entering all traits into the same regression model. Not a single trait predicted the risk of extinction when analysis was conducted without controlling for the other traits. This discrepancy among the results suggests that a single trait may not be enough to allow prediction of the risk of extinction. Instead, it seems that for successful, predictive conservation science data on several ecological characteristics are needed.     

污染场地土壤重金属空间异常值识别及其点源效应的分离

由点源污染导致的土壤重金属空间异常值的分布及点源效应的大小将影响场地风险评估和修复方案的制定,进而影响场地的环境管理.以某典型重金属污染场地为研究区域,以表层土壤Cd含量为研究对象,引入稳健变异函数模型,并筛选出合适的变异函数模型,结合平尾化方法对表层土壤Cd含量空间异常值进行识别和处理,并有效地分离出点源效应.研究表明：研究区表层土壤Cd含量空间变异大且存在明显的空间异常值;CressieHawkins稳健变异函数更适用于对研究区表层土壤Cd含量半方差函数的估计,采用稳健变异函数和平尾化方法能够有效地识别空间异常值和分离点源效应,空间异常值主要分布在受人为活动影响较大的区块;由于准点过程导致的变异较大,分离出的点源效应解释了异常值点位表层土壤Cd含量的大部分.     

植物镉耐性/富集基因的筛选方法

植物耐受/富集镉（Cd）是一个复杂的过程,涉及转运蛋白家族、螯合蛋白家族、抗氧化系统等多个方面的参与。文章综述了植物Cd耐性/富集基因的筛选方法,包括抗性表达文库、Cd抗性突变体的图位克隆、基因差异表达、电子克隆。筛选Cd抗性植物cDNA酵母表达文库可鉴定与金属束缚、隔离及外排相关的膜转运蛋白;筛选重金属敏感拟南芥突变体和图位克隆突变基因可鉴定与谷胱甘肽和植物螯合肽合成相关的酶;第二代测序技术、基因差异表达分析则是鉴定Cd响应基因以及揭示金属型植物与非耐性植物表型差异分子基础的有效方法;电子克隆也被应用在模式植物中鉴定重金属转运蛋白家族的编码基因。在此基础上,简述了Cd耐性/富集基因在改良植物Cd耐性或富集上的应用。     

Framing the search for a theory of land use

ABSTRACT

Land system science and affiliated research linked to sustainability require improved understanding and theorization of land and its change as a social-ecological system (SES). The absence of a general land-use theory, anchored in the social subsystem but with explicit links to the environmental subsystem, hampers this effort. Drawing on land-use explanations, meta-analyses, and associated frameworks, we advance a broad framework structure of eight elements – aggregations of explanatory variables – with links to the biophysical subsystem, for systematic comparisons of extant explanations. Tests and models can be employed to identify which set of variables and their configurations provide robust explanations of across land uses, identifying the potential for theory development. The framework and its application are applicable to both top-down and bottom-up explanatory approaches employed in the social sciences. Links to the environmental subsystem invite future exploration of SES explanations that reach across the different dimensions of global change and sustainability science.     

Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error

There has been a great deal of recent discussion of the practice of regression analysis (or more generally, linear modelling) in behaviour and ecology. In this paper, I wish to highlight two factors that have been under-considered, collinearity and measurement error in predictors, as well as to consider what happens when both exist at the same time. I examine what the consequences are for conventional regression analysis (ordinary least squares, OLS) as well as model averaging methods, typified by information theoretic approaches based around Akaike’s information criterion. Collinearity causes variance inflation of estimated slopes in OLS analysis, as is well known. In the presence of collinearity, model averaging reduces this variance for predictors with weak effects, but also can lead to parameter bias. When collinearity is strong or when all predictors have strong effects, model averaging relies heavily on the full model including all predictors and hence the results from this and OLS are essentially the same. I highlight that it is not safe to simply eliminate collinear variables without due consideration of their likely independent effects as this can lead to biases. Measurement error is also considered and I show that when collinearity exists, this can lead to extreme biases when predictors are collinear, have strong effects but differ in their degree of measurement error. I highlight techniques for dealing with and diagnosing these problems. These results reinforce that automated model selection techniques should not be relied on in the analysis of complex multivariable datasets.     

Egg testosterone affects wattle color and trait covariation in the ring-necked pheasant

Transfer of maternal hormones to the eggs is a major source of offspring phenotypic variation. The developmental and organizational effects of egg hormones can extend into adulthood and affect behavioral and morphological traits involved in sexual and reproductive behavior, with important consequences for offspring fitness. In this study, we injected testosterone (T) in egg albumen of captive ring-necked pheasant (Phasianus colchicus) eggs. We then assessed the consequences for chick growth, cell-mediated immunity, and multiple male secondary sexual traits at maturity by comparison with a control group. We also compared the covariation between traits in the two experimental groups. We found that control males had redder wattles than males from T-injected eggs, suggesting that attractiveness and reproductive success of the offspring might vary depending on maternal transfer of T to the eggs. T treatment also modified the covariation between cell-mediated immunity and wattle coloration and between the area of the wattle and the expression of another secondary sexual trait, the ear tufts. These effects are likely to translate into fitness differences among the offspring if mate acquisition depends on the simultaneous expression of several traits that are differentially affected by the same maternal contribution. Maternal effects mediated by egg hormones might affect the fitness of the offspring not only by directional modification of phenotypic traits, but also by facilitating or inhibiting their covariation. This suggests the possibility that female choice based on the relative expression of multiple secondary sexual traits exerts a pressure on how maternal transfer of androgens contributes to developmental programs.     

Relatedness and environment affect traits associated with invasive and noninvasive introduced Commelinaceae.

Understanding the traits of invasive species may improve the ability to predict, prevent, and manage invasions. I compared morphological and performance traits of five congeneric pairs of invasive and noninvasive Commelinaceae across a factorial experiment using a range of water and nutrient availabilities. Invasive species had greater fecundity and vegetative reproduction than their noninvasive relatives. The invasive species also had higher relative growth rates, greater specific leaf area, and more plastic root-to-shoot ratios than noninvasive species. However, whether a trait was associated with invasiveness often depended on both environment and relatedness. Invasives had greater sexual and vegetative reproduction, higher specific leaf area, and greater relative growth rates than noninvasive congeners, but only in some environments. Differences between invasive and noninvasive taxa were greatest at high nutrient availabilities. These results suggest that studies of invasive species' traits must incorporate information on conditions under which the trait was measured. In addition, incorporating information on relatedness improved our ability to detect associations between species traits, such as specific leaf area and relative growth rate, and invasiveness, suggesting that such information may be required for a complete understanding of what makes a species invasive.     

浑河流域野生鲫鱼卵黄蛋白原基因表达

为揭示浑河流域水环境内分泌干扰物对水生态的潜在风险,利用兼并引物扩增获得鲫鱼卵黄蛋白原(Vtg)和核糖体蛋白L-7(RPL-7)基因部分碱基序列(分别为825和450bp),建立以RPL-7为内参基因、定量鲫鱼Vtg基因表达的实时定量逆转录聚合酶链反应(qRT-PCR)方法,并将该方法用于定量浑河野生鲫鱼肝组织Vtg基因表达分析。结果显示:与上游对照点(S1)相比,7月下游各点(S4～S8)雄鱼、S4和S6点雌鱼肝组织的VtgmRNA表达水平皆显著升高(P<0.05);11月在雄鱼中未检出VtgmRNA的有效表达,雌鱼也仅在S2和S3点的表达水平升高(P<0.05)。研究表明,浑河流域野生鲫鱼尤其是在7月明显受到了环境雌激素类物质的影响。另外,qRT-PCR方法能够灵敏检测出鲫鱼Vtg基因表达的时空差异。     

Analyzing and modelling spatial distribution of summering lesser kestrel: The role of spatial autocorrelation

In modelling spatial distribution of species, ignoring spatial autocorrelation (SA) and multicollinearity may lead to false ecological conclusions. Here we take into account both issues for examining and modelling the spatial pattern of abundance of the globally threatened lesser kestrel (Falco naumanni) during summer in a 38,400 ha area of northwestern Spain where large premigratory aggregations of the species occur. Spatial pattern was examined using Moran's correlogram, and models were built including geographical coordinates and autocovariate terms (which account for SA) in generalized linear models (GLM) and hierarchical partitioning (HP) models. HP models allow to alleviate multicollinearity. A grid-based approach was used by dividing the study area in 24 contiguous 4 km × 4 km squares where birds were counted in 2–3 visits per square (response variable). Environmental coarse-grained variables were extracted from a geographic information system (GIS) at three spatial extents. Moran's correlogram showed that lesser kestrel mean abundance per square was spatially autocorrelated up to 4–8 km. The results from both GLM and HP analyses were roughly compatible. The GLM models explained 80.0% of the variation in kestrel abundance and were the same at the three spatial extents. Lesser Kestrel abundance was not significantly explained by landscape variables, but was negatively related to both the distance to the nearest communal roost and distance to the nearest breeding colony with more of 10 breeding pairs of lesser kestrel. An autocovariate term added later in the GLM models improved both their explanatory power (from 74.5 to 80.0%) and model residuals, which were not longer spatially autocorrelated, fulfilling thus the statistical assumption of independent errors. Findings suggest that the spatial distribution of abundance of summering lesser kestrel is, at least, partially driven by endogenous causes, such as conspecific attraction. Exogenous causes such as finer-scale variables (e.g. type of crops and food available) are yet likely needed for lesser kestrel-environment relationships.     

Forward selection of explanatory variables

This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. To prevent the first problem, we propose a two-step procedure. First, a global test using all explanatory variables is carried out. If, and only if, the global test is significant, one can proceed with forward selection. To prevent overestimation of the explained variance, the forward selection has to be carried out with two stopping criteria: (1) the usual alpha significance level and (2) the adjusted coefficient of multiple determination (Ra(2)) calculated using all explanatory variables. When forward selection identifies a variable that brings one or the other criterion over the fixed threshold, that variable is rejected, and the procedure is stopped. This improved method is validated by simulations involving univariate and multivariate response data. An ecological example is presented using data from the Bryce Canyon National Park, Utah, U.S.A.     

Regression-type estimators for adaptive two-stage sequential sampling

Adaptive two-stage sequential sampling (ATSSS) design was developed to observe more rare units and gain higher efficiency, in the sense of having a smaller variance estimator, than conventional sampling designs with equal effort for rare and spatially cluster populations. For certain rare populations, incorporating auxiliary variables into a sampling design can further improve the observation of rare units and increase efficiency. In this article, we develop regression-type estimators for ATSSS so that auxiliary variables can be incorporated into the ATSSS design when warranted. Simulation studies on two populations show that the regression-type estimators can significantly increase the efficiency of ATSSS and the detection of more rare units as compared to conventional sampling counterparts. Simulation of sampling of desert shrubs in Inner Mongolia (one of the two populations studied) showed that by incorporating a GIS auxiliary variable into ATSSS with the regression estimators resulted in a gain in efficiency over ATSSS without the auxiliary variable. Further, we found that the use of the GIS auxiliary variable in a conventional two-stage design with a regression estimator did not show a gain in efficiency.     

Functional interactions among tortoise beetle larval defenses reveal trait suites and escalation

Whereas the “escape-and-radiate” plant-herbivore scenario predicts that reciprocating cycles of defense-counter defense foster the evolution of traits with increasing efficacy that accumulate during clade diversification, coevolutionary models of herbivore responses to their enemies remain unexplored. Quantitative information is scarce about how defensive traits perform, interact and become functionally integrated. Moreover, there are few studies that have combined performance and phylogenetic information to detect patterns of trait assembly and trends in defense efficacy. Using field demonstrations of effectiveness and phylogenetic reconstructions, we evaluated patterns of trait precedence and suite assembly by comparing the larval defenses of two beetles, Acromis sparsa and Chelymorpha alternans, which both feed on the leaf surfaces of the same plant, have shields containing host-derived deterrent chemicals and form aggregations. Additionally, female A. sparsa guard their larvae. Using an ecologically relevant bioassay, we quantified the extent to which: (1) gregariousness, size, maternal care and shields affected survival; (2) defenses interacted, and; (3) derived traits and suites outperformed ancestral ones. Regression models ranked traits revealing synergistic interactions. Shields interacted with gregariousness to form the strongest suite. Maternal care contributed to overall higher survival in A. sparsa, an advantage lost after female removal. Phylogenetic reconstruction revealed a sequence of trait accumulation and suite formation. The combined performance-phylogenetic approach revealed: (1) multi-trait interactions amplified effectiveness; (2) a sequence of novel trait origins was followed by suite assembly, and; (3) an incremental trend in defense efficacy congruent with escalation. Multi-trait interactions fostered suite assembly that likely conferred the advantage of enhanced survival in the precarious leaf surface adaptive zone.