Spatial segregation of sympatric marten and fishers: the influence of landscapes and species‐scapes

Co‐occurring species are rarely considered as a factor influencing habitat selection. However, niche theory predicts that sharing resources, predators, and other interspecific interactions can limit the environmental conditions under which a species may exist. How does the spatial distribution of one species affect that of another within shared landscapes? We tested whether sympatric marten Martes americana and fishers M. pennanti in a mountain landscape in Alberta, Canada exhibit local‐scale spatial segregation, beyond differential habitat selection. We modelled marten and fisher distribution in relation to remotely‐sensed habitat data and species co‐occurrence, using generalized linear models and information‐theoretic model selection. Marten and fishers selected different habitat types and showed different responses to habitat fragmentation. Even after accounting for these differences, the absence of one species significantly explained the occurrence of the other. We conclude that the spatial distribution of marten and fishers influences habitat selection by each other at landscape scales, and hypothesize that this pattern may result from competition in a spatially heterogeneous environment. Species‐habitat models that consider only resources may fail to capture key predictors of species’ occurrence. Reliable prediction and inference requires that ecologists expand from landscapes to also include species‐scapes: a spatial plane of species interactions that combines with resources to drive species’ distributions.     

Fruit selection by Andean forest birds: influence of fruit functional traits and their temporal variation

Fruit selection, i.e., the consumption of fruits disproportionately to their availability, results from the interaction between diet preferences and ecological factors that modify them. We assessed the importance of functional fruit traits to explain fruit selection by birds in Andean subtropical forests, taking into account temporal variation in trait distribution in the assembly of available fruits. During 2 yr, we measured the abundance of ripe fruits and their consumption by birds in a 6‐ha plot during 11 bimonthly samplings, and we used 17 phenological, morphological, and nutritional traits to characterize fruits selected by four bird species. Fruit selection was pervasive year‐round, highly variable over time and across bird species. Fruit species were selected over time periods shorter than their ripening phenology, and the selection of fruits with particular traits was specific to the fruit‐eating species. Maximization in pulp reward per consumed fruit seems to be the main driving force behind fruit selection, indicating that birds select fruits with traits that directly affect net energy gain. Our results can be interpreted in a framework of a hierarchy of foraging decisions, under which the spatiotemporal context of the fruiting environment modifies the relative intake rates of a particular fruit, while the ability to discriminate fruit contents becomes increasingly important on a smaller dimension. We show that fruit‐selection properties are contingent on specific fruit traits and particular spatiotemporal conditions, which modify the structure of mutualistic interactions.     

A comparison of model selection methods for prediction in the presence of multiply imputed data

Many approaches for variable selection with multiply imputed data in the development of a prognostic model have been proposed. However, no method prevails as uniformly best. We conducted a simulation study with a binary outcome and a logistic regression model to compare two classes of variable selection methods in the presence of MI data: (I) Model selection on bootstrap data, using backward elimination based on AIC or lasso, and fit the final model based on the most frequently (e.g. ) selected variables over all MI and bootstrap data sets; (II) Model selection on original MI data, using lasso. The final model is obtained by (i) averaging estimates of variables that were selected in any MI data set or (ii) in 50% of the MI data; (iii) performing lasso on the stacked MI data, and (iv) as in (iii) but using individual weights as determined by the fraction of missingness. In all lasso models, we used both the optimal penalty and the 1‐se rule. We considered recalibrating models to correct for overshrinkage due to the suboptimal penalty by refitting the linear predictor or all individual variables. We applied the methods on a real dataset of 951 adult patients with tuberculous meningitis to predict mortality within nine months. Overall, applying lasso selection with the 1‐se penalty shows the best performance, both in approach I and II. Stacking MI data is an attractive approach because it does not require choosing a selection threshold when combining results from separate MI data sets     

Climate and vegetation hierarchically structure patterns of songbird distribution in the Canadian boreal region

Environmental factors controlling the distribution and abundance of boreal avifauna are not fully understood, limiting our ability to predict the consequences of a changing climate and industrial development activities underway. We used a compilation of avian point‐count data, collected over 1990–2008 from nearly 36 000 locations, to model the abundance of individual forest songbird species within the Canadian boreal forest. We evaluated 30 vegetation and 101 climatic variables, representing most of the widely‐used dimensions of climate space, along with less usual measures of inter‐annual variability. Regression tree models allowed us to calculate the relative importance of climate and vegetation variable classes according to avian migration strategy without the need for a priori variable selection or dimension reduction. We tested for hierarchical habitat selection by formulating hypotheses on the locations of variables within the model tree structures. Climate variables explained the majority (77%) of deviance explained over 98 species modelled. As may be expected at high latitudes, we found energy availability (temperature, 65%) to be more important than moisture availability (precipitation, 12%). The contributions of inter‐ and intra‐annual climate variability (28%) were about half that of mean conditions. The relatively large contribution of remotely‐sensed vegetation metrics (23%) highlighted the importance of local vegetation heterogeneity controlled by non‐climatic factors. The two most important vegetation variables were landcover type and April leaf area index. When selected, these generally occurred in a model's right subtree, consistent with predictions from hierarchical habitat selection theory. When occupying the root node, landcover effectively delineated the historical forest‐prairie ecotone, reflecting the current disequilibrium between climate and vegetation due to human land use. Our findings suggest a large potential for avian distributional shifts in response to climate change, but also demonstrate the importance of finer scale vegetation heterogeneity in the spatial distribution of boreal birds.     

Predicting species distributions: a critical comparison of the most common statistical models using artificial species

Aim To test statistical models used to predict species distributions under different shapes of occurrence–environment relationship. We addressed three questions: (1) Is there a statistical technique that has a consistently higher predictive ability than others for all kinds of relationships? (2) How does species prevalence influence the relative performance of models? (3) When an automated stepwise selection procedure is used, does it improve predictive modelling, and are the relevant variables being selected? Location We used environmental data from a real landscape, the state of California, and simulated species distributions within this landscape. Methods Eighteen artificial species were generated, which varied in their occurrence response to the environmental gradients considered (random, linear, Gaussian, threshold or mixed), in the interaction of those factors (no interaction vs. multiplicative), and on their prevalence (50% vs. 5%). The landscape was then randomly sampled with a large (n = 2000) or small (n = 150) sample size, and the predictive ability of each statistical approach was assessed by comparing the true and predicted distributions using five different indexes of performance (area under the receiver‐operator characteristic curve, Kappa, correlation between true and predictive probability of occurrence, sensitivity and specificity). We compared generalized additive models (GAM) with and without flexible degrees of freedom, logistic regressions (general linear models, GLM) with and without variable selection, classification trees, and the genetic algorithm for rule‐set production (GARP). Results Species with threshold and mixed responses, additive environmental effects, and high prevalence generated better predictions than did other species for all statistical models. In general, GAM outperforms all other strategies, although differences with GLM are usually not significant. The two variable‐selection strategies presented here did not discriminate successfully between truly causal factors and correlated environmental variables. Main conclusions Based on our analyses, we recommend the use of GAM or GLM over classification trees or GARP, and the specification of any suspected interaction terms between predictors. An expert‐based variable selection procedure was preferable to the automated procedures used here. Finally, for low‐prevalence species, variability in model performance is both very high and sample‐dependent. This suggests that distribution models for species with low prevalence can be improved through targeted sampling.     

Plant–mycorrhizal fungus co‐occurrence network lacks substantial structure

The interactions between plants and arbuscular mycorrhizal fungi (AMF) maintain a crucial link between macroscopic organisms and the soil microbial world. These interactions are of extreme importance for the diversity of plant communities and ecosystem functioning. Despite this importance, only recently has the structure of plant–AMF interaction networks been studied. These recent studies, which used genetic data, suggest that these networks are highly structured, very similar to plant–animal mutualistic networks. However, the assembly process of plant–AMF communities is still largely unknown, and an important feature of plant–AMF interactions has not been incorporated: they occur at an extremely localized scale. Studying plant–AMF networks in a spatial context seems therefore a crucial step. This paper studies a plant–AMF spatial co‐occurrence network using novel methodology based on information theory and a unique set of spatially explicit species‐level data. We apply three null models of which only one accounts for spatial effects. We find that the data show substantial departures from null expectations for the two non‐spatial null models. However, for the null model considering spatial effects, there are few significant co‐occurrences compared with the other two null models. Thus, plant–AMF spatial co‐occurrences seem to be mostly explained by stochasticity, with a small role for other factors related to plant–AMF specialization. Furthermore, we find that the network is not significantly nested or modular. We conclude that this plant–AMF spatial co‐occurrence network lacks substantial structure and, therefore, plants and AMF species do not track each other over space. Thus, random encounters seem more important in the first step of the assembly of plant–AMF communities. Synthesis The symbiotic interaction between plants and arbuscular mycorrhizal fungi (AMF) is crucial for ecosystem functioning. However, the factors affecting the assembly of plant‐AMF communities are poorly understood. An important factor of the assembly of plant‐AMF communities has been overlooked: plant‐AMF interactions occur at a localized spatial scale. Our study investigated the importance of space in the structure of plant‐AMF communities. We studied a plant‐AMF spatial co‐occurrence network using a unique set of spatially explicit data and applied three null models. We found that plant‐AMF spatial co‐occurrences seem to be mostly explained by stochasticity. In particular, our study shows that this plant‐AMF spatial co‐occurrence network lacks substantial structure and, therefore, plants and AMF species do not track each other over space. Thus, random encounters seem to drive the assembly of plant‐AMF communities.     

Dynamic species co‐occurrence networks require dynamic biodiversity surrogates

In conservation it is inevitable that surrogates be selected to represent the occurrence of hard‐to‐find species and find priority locations for management. However, species co‐occurrence can vary over time. Here we demonstrate how temporal dynamics in species co‐occurrence influence the ability of managers to choose the best surrogate species. We develop an efficient optimisation formulation that selects the optimal set of complementary surrogate species from any co‐occurrence network. We apply it to two Australian datasets on successional bird responses to disturbances of revegetation and fire. We discover that a surprisingly small number of species are required to represent the majority of species co‐occurrences at any one time. Because co‐occurrence patterns are temporally dynamic, the optimal set of surrogates, and the number of surrogates required to achieve a desired surrogacy power, depend on sampling effort and the successional state of a system. Overlap in optimal sets of surrogates for representing 70% of co‐occurring species ranges from zero to 57% depending on when the surrogacy decision is made. Surrogate sets representing early successional communities over‐estimate the power of surrogacy decisions at later times. Our results show that in dynamic systems, optimal surrogates might be selected in different ways: 1) use short‐term monitoring to choose a larger number of static less‐informative surrogates; 2) use long‐term monitoring to choose a smaller number of static high‐power surrogates that may poorly represent early successional co‐occurrence; 3) develop adaptive surrogate selection frameworks with high short‐term and long‐term surrogacy power that update surrogate sets and capture temporal dynamics in species co‐occurrence. Our results suggest vigilance is needed when selecting surrogates for other co‐occurring species in dynamic landscapes, as selected surrogates from one time may have reduced effectiveness at a different time. Ultimately, decisions that fail to acknowledge dynamic species co‐occurrence will lead to uninformative or redundant surrogates.     

Balance between site fidelity and habitat preferences in colony site selection by herons and egrets

Habitat selection in avian species is a hierarchical process driven by different factors acting at multiple scales. Habitat preferences and site fidelity are two main factors affecting how colonial birds choose their breeding locations. Although these two factors affect how colonial species choose their habitats, previous studies have only focused on one factor at a time to explain the distribution of species at regional scales. Here we used 28 yr of colony location data of herons and egrets around Ibaraki prefecture in Japan in order to analyze the relative importance of habitat preferences and colony site fidelity for selecting breeding locations. We used Landsat satellite images together with a ground survey‐based map to create land‐use maps for past years and determine the habitats surrounding the herons and egrets colonies. Combining the estimated colony site fidelity with the habitat data, we used a random forest algorithm to create habitat selection models, which allowed us to analyze the changes in the importance of those factors over the years. We found high levels of colony site fidelity for each year of study, with its relative importance as a predictor for explaining colony distribution increasing drastically in the most recent five years. The increase in collective site fidelity could have been caused by recent changes in the population size of grey herons Ardea cinerea, a key species for colony establishment. We observed a balance between habitat preferences and colony site fidelity: habitat preferences were a more powerful predictor of colony distribution until 2008, when colony site fidelity levels were lower. Considering changes in the relative importance of these factors can lead to a better understanding of the habitat selection process and help to analyze bird species’ responses to environmental changes.     

Optimal taxonomic groups for biodiversity assessment: a meta‐analytic approach

A fundamental decision in biodiversity assessment is the selection of one or more study taxa, a choice that is often made using qualitative criteria such as historical precedent, ease of detection, or available technical or taxonomic expertise. A more robust approach would involve selecting taxa based on the a priori expectation that they will provide the best possible information on unmeasured groups, but data to inform such hypotheses are often lacking. Using a global meta‐analysis, we quantified the proportion of variability that each of 12 taxonomic groups (at the Order level or above) explained in the richness or composition of other taxa. We then applied optimization to matrices of pairwise congruency to identify the best set of complementary surrogate groups. We found that no single taxon was an optimal surrogate for both the richness and composition of unmeasured taxa if we used simple methods to aggregate congruence data between studies. In contrast, statistical methods that accounted for well‐known drivers of cross‐taxon congruence (spatial extent, grain size, and latitude) lead to the prioritization of similar surrogates for both species richness and composition. Advanced statistical methods were also more effective at describing known ecological relationships between taxa than simple methods, and show that congruence is typically highest between taxonomically and functionally dissimilar taxa. Birds and vascular plants were most frequently selected by our algorithm as surrogates for other taxonomic groups, but the extent to which any one taxon was the ‘optimal’ choice of surrogate for other biodiversity was highly context‐dependent. In the absence of other information – such as in data‐poor areas of the globe, and under limited budgets for monitoring or assessment – ecologists can use our results to assess which taxa are most likely to reflect the distribution of the richness or composition of ‘total’ biodiversity.     

Latitudinal gradients and the shaping of life‐history traits in a gregarious caterpillar

The present study aimed to investigate how the impact of several factors linked to geography would shape life‐history traits in a gregarious species, using the pine processionary moth (PPM) Thaumetopoea pityocampa as a model system. PPM has a wide geographical distribution over the Mediterranean Basin, and it is a strictly gregarious species throughout larval development, where the total reproductive output of each female forms a colony. We reviewed both published and unpublished data on PPM from all over its distribution in the Mediterranean Basin and extracted data on fecundity, egg size, egg parasitoid mortality, flight period, and development time. These life‐history traits were then related to location, expressed as latitude and altitude, local average temperatures, and host tree species. We found that PPM fecundity increaseed with latitude, concomitant with an increase in the length of development and an earlier onset of adult flight. These results are the opposite of that found in other Lepidoptera species with a wide geographical distribution, as well as in insects in general. We propose that a large colony size in PPM is important at higher latitudes because this confers an advantage for thermoregulation and tent building in areas where larvae have to face harsher conditions during the winter, thus shifting the optimal trade‐off between the number and size of eggs with latitude. However, host tree species also affected the relationship between egg number and size and the optimal outcome of these traits is likely a compromise between different selection pressures. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100 , 224–236.     

Maximally selected chi-square statistics for ordinal variables

The association between a binary variable Y and a variable X having an at least ordinal measurement scale might be examined by selecting a cutpoint in the range of X and then performing an association test for the obtained 2 x 2 contingency table using the chi-square statistic. The distribution of the maximally selected chi-square statistic (i.e. the maximal chi-square statistic over all possible cutpoints) under the null-hypothesis of no association between X and Y is different from the known chi-square distribution. In the last decades, this topic has been extensively studied for continuous X variables, but not for non-continuous variables of at least ordinal measurement scale (which include e.g. classical ordinal or discretized continuous variables). In this paper, we suggest an exact method to determine the finite-sample distribution of maximally selected chi-square statistics in this context. This novel approach can be seen as a method to measure the association between a binary variable and variables having an at least ordinal scale of different types (ordinal, discretized continuous, etc). As an illustration, this method is applied to a new data set describing pregnancy and birth for 811 babies.     

Bayesian Variable Selection for Multivariate Spatially Varying Coefficient Regression

Summary Physical activity has many well‐documented health benefits for cardiovascular fitness and weight control. For pregnant women, the American College of Obstetricians and Gynecologists currently recommends 30 minutes of moderate exercise on most, if not all, days; however, very few pregnant women achieve this level of activity. Traditionally, studies have focused on examining individual or interpersonal factors to identify predictors of physical activity. There is a renewed interest in whether characteristics of the physical environment in which we live and work may also influence physical activity levels. We consider one of the first studies of pregnant women that examines the impact of characteristics of the built environment on physical activity levels. Using a socioecologic framework, we study the associations between physical activity and several factors including personal characteristics, meteorological/air quality variables, and neighborhood characteristics for pregnant women in four counties of North Carolina. We simultaneously analyze six types of physical activity and investigate cross‐dependencies between these activity types. Exploratory analysis suggests that the associations are different in different regions. Therefore, we use a multivariate regression model with spatially varying regression coefficients. This model includes a regression parameter for each covariate at each spatial location. For our data with many predictors, some form of dimension reduction is clearly needed. We introduce a Bayesian variable selection procedure to identify subsets of important variables. Our stochastic search algorithm determines the probabilities that each covariate's effect is null, non‐null but constant across space, and spatially varying. We found that individual‐level covariates had a greater influence on women's activity levels than neighborhood environmental characteristics, and some individual‐level covariates had spatially varying associations with the activity levels of pregnant women.     

Substrate selection for pit making and oviposition in an antlion, Myrmeleon bore Tjeder, in terms of sand particle size

The larvae of the pit‐making antlion Myrmeleon bore Tjeder live in open sand in riverbeds with a substratum consisting of various particle sizes. We analyzed the spatial distribution of their pits in a sandy floodplain to determine their larval and adult responses to the heterogeneous substrate. The spatial distribution pattern of their pits had an aggregated distribution, and there was a significant positive correlation between pit density and the ratio of medium‐size sand particles to total weight of sand. We examined the size of sand particles selected in the larval pit‐building behavior and the oviposition behavior of the adult. Both larvae and adults selected medium‐size sand particles. The larvae of M. bore are relatively sedentary predators and rarely move great distances. Thus, the present results suggest that habitat selection by adult females is a major factor causing the aggregative distribution of the pits.     

Testing for Treatment Effects on Subsets of Endpoints

Multiple endpoints are tested to assess an overall treatment effect and also to identify which endpoints or subsets of endpoints contributed to treatment differences. The conventional p‐value adjustment methods, such as single‐step, step‐up, or step‐down procedures, sequentially identify each significant individual endpoint. Closed test procedures can also detect individual endpoints that have effects via a step‐by‐step closed strategy. This paper proposes a global‐based statistic for testing an a priori number, say, r of the k endpoints, as opposed to the conventional approach of testing one (r = 1) endpoint. The proposed test statistic is an extension of the single‐step p‐value‐based statistic based on the distribution of the smallest p‐value. The test maintains strong control of the FamilyWise Error (FWE) rate under the null hypothesis of no difference in any (sub)set of r endpoints among all possible combinations of the k endpoints. After rejecting the null hypothesis, the individual endpoints in the sets that are rejected can be tested further, using a univariate test statistic in a second step, if desired. However, the second step test only weakly controls the FWE. The proposed method is illustrated by application to a psychosis data set.     

Testing for Homogeneity in Meta‐Analysis I. The One‐Parameter Case: Standardized Mean Difference

Summary Meta‐analysis seeks to combine the results of several experiments in order to improve the accuracy of decisions. It is common to use a test for homogeneity to determine if the results of the several experiments are sufficiently similar to warrant their combination into an overall result. Cochran’s Q statistic is frequently used for this homogeneity test. It is often assumed that Q follows a chi‐square distribution under the null hypothesis of homogeneity, but it has long been known that this asymptotic distribution for Q is not accurate for moderate sample sizes. Here, we present an expansion for the mean of Q under the null hypothesis that is valid when the effect and the weight for each study depend on a single parameter, but for which neither normality nor independence of the effect and weight estimators is needed. This expansion represents an order O(1/n) correction to the usual chi‐square moment in the one‐parameter case. We apply the result to the homogeneity test for meta‐analyses in which the effects are measured by the standardized mean difference (Cohen’s d‐statistic). In this situation, we recommend approximating the null distribution of Q by a chi‐square distribution with fractional degrees of freedom that are estimated from the data using our expansion for the mean of Q. The resulting homogeneity test is substantially more accurate than the currently used test. We provide a program available at the Paper Information link at the Biometrics website http://www.biometrics.tibs.org for making the necessary calculations.     

Pairwise Variable Selection for High‐Dimensional Model‐Based Clustering

Summary Variable selection for clustering is an important and challenging problem in high‐dimensional data analysis. Existing variable selection methods for model‐based clustering select informative variables in a “one‐in‐all‐out” manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it cannot separate any of the clusters. In many applications, however, it is of interest to further establish exactly which clusters are separable by each informative variable. To address this question, we propose a pairwise variable selection method for high‐dimensional model‐based clustering. The method is based on a new pairwise penalty. Results on simulated and real data show that the new method performs better than alternative approaches that use ?₁ and ?_∞ penalties and offers better interpretation.     

Moult topography and its application to the study of partial wing‐moult in two neotropical wrens

During partial moults birds replace a variable number or percentage of old feathers. This quantity, known as moult extent, has been a primary variable used in comparative studies. However, different spatial configurations of feather replacement may result from an equal number of renewed feathers. Few studies have addressed spatial aspects of moult, which may vary among species, among individuals of the same species and between episodes at the individual level. We present a novel approach to quantify the spatial configuration of a wing‐moult episode, hereafter referred to as moult topography, which comprises two elements, namely extent and vector, the latter condensing the spatial configuration of the replaced feathers on the wing plane. We apply this method to investigate preformative (post‐juvenile) wing‐feather moult pattern in the Spot‐breasted Wren Pheugopedius maculipectus and the White‐breasted Wood‐Wren Henicorhina leucosticta. We specified a null model of wing‐moult topography by which feather replacement follows a discrete anterior–posterior (vertical) axis between tracts and a discrete proximal–distal (horizontal) axis within tracts, and whereby wing feathers from a new tract are replaced only if all the feathers from the previous (anterior) tract have been replaced. Our sample of Spot‐breasted Wrens showed a strict single pattern of replacement that did not differ significantly from the null model. Our sample of White‐breasted Wood‐Wrens, however, differed significantly from the null model, showing prioritization of proximal wing feathers closer to the body. These differences might have biological relevance, for example in mate selection or in response to different environmental stressors, and might reveal the influence of these factors on the evolution of moult strategies. Overall, moult topography provides a new approach to future ecological and evolutionary studies of moult.     

Regression on Quantile Residual Life

Summary A time‐specific log‐linear regression method on quantile residual lifetime is proposed. Under the proposed regression model, any quantile of a time‐to‐event distribution among survivors beyond a certain time point is associated with selected covariates under right censoring. Consistency and asymptotic normality of the regression estimator are established. An asymptotic test statistic is proposed to evaluate the covariate effects on the quantile residual lifetimes at a specific time point. Evaluation of the test statistic does not require estimation of the variance–covariance matrix of the regression estimators, which involves the probability density function of the survival distribution with censoring. Simulation studies are performed to assess finite sample properties of the regression parameter estimator and test statistic. The new regression method is applied to a breast cancer data set with long‐term follow‐up to estimate the patients' median residual lifetimes, adjusting for important prognostic factors.     

Dynamic clinical prediction models for discrete time‐to‐event data with competing risks—A case study on the OUTCOMEREA database

The development of clinical prediction models requires the selection of suitable predictor variables. Techniques to perform objective Bayesian variable selection in the linear model are well developed and have been extended to the generalized linear model setting as well as to the Cox proportional hazards model. Here, we consider discrete time‐to‐event data with competing risks and propose methodology to develop a clinical prediction model for the daily risk of acquiring a ventilator‐associated pneumonia (VAP) attributed to P. aeruginosa (PA) in intensive care units. The competing events for a PA VAP are extubation, death, and VAP due to other bacteria. Baseline variables are potentially important to predict the outcome at the start of ventilation, but may lose some of their predictive power after a certain time. Therefore, we use a landmark approach for dynamic Bayesian variable selection where the set of relevant predictors depends on the time already spent at risk. We finally determine the direct impact of a variable on each competing event through cause‐specific variable selection.