Effect of field sampling design on variation partitioning in a dendritic stream network

Variation partitioning is one of the most frequently used method to infer the importance of environmental (niche based) and spatial (dispersal) processes in metacommunity structuring. However, the reliability of the method in predicting the role of the major structuring forces is less known. We studied the effect of field sampling design on the result of variation partitioning of fish assemblages in a stream network. Along with four different sample sizes, a simple random sampling from a total of 115 stream segments (sampling objects) was applied in 400 iterations, and community variation of each random sample was partitioned into four fractions: pure environmentally (landscape variables) explained, pure spatially (MEM eigenvectors) explained, jointly explained by environment and space, and unexplained variance. Results were highly sensitive to sample size. Even at a given sample size, estimated variance fractions had remarkable random fluctuation, which can lead to inconsistent results on the relative importance of environmental and spatial variables on the structuring of metacommunities. Interestingly, all the four variance fractions correlated better with the number of the selected spatial variables than with any design properties. Sampling interval proved to be a fundamentally influential sampling design property because it affected the number of the selected spatial variables. Our findings suggest that the effect of sampling design on variation partitioning is related to the ability of the eigenvectors to model complex spatial patterns. Hence, properties of the sampling design should be more intensively considered in metacommunity studies.     

Recent developments in the analysis of comparative data

Comparative methods can be used to test ideas about adaptation by identifying cases of either parallel or convergent evolutionary change across taxa. Phylogenetic relationships must be known or inferred if comparative methods are to separate the cross-taxonomic covariation among traits associated with evolutionary change from that attributable to common ancestry. Only the former can be used to test ideas linking convergent or parallel evolutionary change to some aspect of the environment. The comparative methods that are currently available differ in how they manage the effects brought about by phylogenetic relationships. One method is applicable only to discrete data, and uses cladistic techniques to identify evolutionary events that depart from phylogenetic trends. Techniques for continuous variables attempt to control for phylogenetic effects in a variety of ways. One method examines the taxonomic distribution of variance to identify the taxa within which character variation is small. The method assumes that taxa with small amounts of variation are those in which little evolutionary change has occurred, and thus variation is unlikely to be independent of ancestral trends. Analyses are then concentrated among taxa that show more variation, on the assumption that greater evolutionary change in the character has taken place. Several methods estimate directly the extent to which ancestry can predict the observed variation of a character, and subtract the ancestral effect to reveal variation of phylogeny. Yet another can remove phylogenetic effects if the true phylogeny is known. One class of comparative methods controls for phylogenetic effects by searching for comparative trends within rather than across taxa. With current knowledge of phylogenies, there is a trade-off in the choice of a comparative method: those that control phylogenetic effects with greater certainty are either less applicable to real data, or they make restrictive or untestable assumptions. Those that rely on statistical patterns to infer phylogenetic effects may not control phylogeny as efficiently but are more readily applied to existing data sets.     

Effects of sample size and intraspecific variation in phylogenetic comparative studies: a meta‐analytic review

Comparative analyses aim to explain interspecific variation in phenotype among taxa. In this context, phylogenetic approaches are generally applied to control for similarity due to common descent, because such phylogenetic relationships can produce spurious similarity in phenotypes (known as phylogenetic inertia or bias). On the other hand, these analyses largely ignore potential biases due to within‐species variation. Phylogenetic comparative studies inherently assume that species‐specific means from intraspecific samples of modest sample size are biologically meaningful. However, within‐species variation is often significant, because measurement errors, within‐ and between‐individual variation, seasonal fluctuations, and differences among populations can all reduce the repeatability of a trait. Although simulations revealed that low repeatability can increase the type I error in a phylogenetic study, researchers only exercise great care in accounting for similarity in phenotype due to common phylogenetic descent, while problems posed by intraspecific variation are usually neglected. A meta‐analysis of 194 comparative analyses all adjusting for similarity due to common phylogenetic descent revealed that only a few studies reported intraspecific repeatabilities, and hardly any considered or partially dealt with errors arising from intraspecific variation. This is intriguing, because the meta‐analytic data suggest that the effect of heterogeneous sampling can be as important as phylogenetic bias, and thus they should be equally controlled in comparative studies. We provide recommendations about how to handle such effects of heterogeneous sampling.     

Accuracy, precision, and economic efficiency for three methods of thrips (Thysanoptera: Thripidae) population density assessment

Western flower thrips, Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), is a major horticultural pest and an important vector of plant viruses in many parts of the world. Methods for assessing thrips population density for pest management decision support are often inaccurate or imprecise due to thrips' positive thigmotaxis, small size, and naturally aggregated populations. Two established methods, flower tapping and an alcohol wash, were compared with a novel method, plant desiccation coupled with passive trapping, using accuracy, precision and economic efficiency as comparative variables. Observed accuracy was statistically similar and low (37.8-53.6%) for all three methods. Flower tapping was the least expensive method, in terms of person-hours, whereas the alcohol wash method was the most expensive. Precision, expressed by relative variation, depended on location within the greenhouse, location on greenhouse benches, and the sampling week, but it was generally highest for the flower tapping and desiccation methods. Economic efficiency, expressed by relative net precision, was highest for the flower tapping method and lowest for the alcohol wash method. Advantages and disadvantages are discussed for all three methods used. If relative density assessment methods such as these can all be assumed to accurately estimate a constant proportion of absolute density, then high precision becomes the methodological goal in terms of measuring insect population density, decision making for pest management, and pesticide efficacy assessments.     

A comparison of diversity estimators applied to a database of host–parasite associations

Understanding the drivers of biodiversity is important for forecasting changes in the distribution of life on earth. However, most studies of biodiversity are limited by uneven sampling effort, with some regions or taxa better sampled than others. Numerous methods have been developed to account for differences in sampling effort, but most methods were developed for systematic surveys in which all study units are sampled using the same design and assemblages are sampled randomly. Databases compiled from multiple sources, such as from the literature, often violate these assumptions because they are composed of studies that vary widely in their goals and methods. Here, we compared the performance of several popular methods for estimating parasite diversity based on a large and widely used parasite database, the Global Mammal Parasite Database (GMPD). We created artificial datasets of host–parasite interactions based on the structure of the GMPD, then used these datasets to evaluate which methods best control for differential sampling effort. We evaluated the precision and bias of seven methods, including species accumulation and nonparametric diversity estimators, compared to analyzing the raw data without controlling for sampling variation. We find that nonparametric estimators, and particularly the Chao2 and second-order jackknife estimators, perform better than other methods. However, these estimators still perform poorly relative to systematic sampling, and effect sizes should be interpreted with caution because they tend to be lower than actual effect sizes. Overall, these estimators are more effective in comparative studies than for producing true estimates of diversity. We make recommendations for future sampling strategies and statistical methods that would improve estimates of global parasite diversity.     

Testing Bergmann's rule and the resource seasonality hypothesis in Malagasy primates using GIS-based climate data

We tested four major hypotheses on the ecological aspects of body mass variation in extant Malagasy strepsirrhines: thermoregulation, resource seasonality/scarcity, resource quality, and primary productivity. These biogeographic hypotheses focus on the ecological aspects of body mass variation, largely ignoring the role of phylogeny for explaining body mass variation within lineages. We tested the independent effects of climate and resource-related variables on variation in body mass among Malagasy primates using recently developed comparative methods that account for phylogenetic history and spatial autocorrelation. We extracted data on lemur body mass and climate variables for a total of 43 species from 39 sites. Climatic data were obtained from the WorldClim database, which is based on climate data from weather stations compiled around the world. Using generalized linear models that incorporate parameters to account for phylogenetic and spatial autocorrelation, we found that diet and climate variables were weak predictors of lemur body mass. Moreover, there was a strong phylogenetic effect relative to the effects of space on lemur body mass in all models. Thus, we failed to find support for any of the four hypotheses on patterns of geography and body mass in extant strepsirrhines. Our results indicate that body mass has been conserved since early in the evolutionary history of each genus, while species diversified into different environmental niches. Our findings are in contrast to some previous studies that have suggested resource and climate related effects on body mass, though these studies have examined this question at different taxonomic and/or geographic scales.     

Fish distribution predictions from different points of view: comparing associative neural networks,geostatistics and regression models

Accurate prediction of species distributions based on sampling and environmental data is essential for further scientific analysis, such as stock assessment, detection of abundance fluctuation due to climate change or overexploitation, and to underpin management and legislation processes. The evolution of computer science and statistics has allowed the development of sophisticated and well-established modelling techniques as well as a variety of promising innovative approaches for modelling species distribution. The appropriate selection of modelling approach is crucial to the quality of predictions about species distribution. In this study, modelling techniques based on different approaches are compared and evaluated in relation to their predictive performance, utilizing fish density acoustic data. Generalized additive models and mixed models amongst the regression models, associative neural networks (ANNs) and artificial neural networks ensemble amongst the artificial neural networks and ordinary kriging amongst the geostatistical techniques are applied and evaluated. A verification dataset is used for estimating the predictive performance of these models. A combination of outputs from the different models is applied for prediction optimization to exploit the ability of each model to explain certain aspects of variation in species acoustic density. Neural networks and especially ANNs appear to provide more accurate results in fitting the training dataset while generalized additive models appear more flexible in predicting the verification dataset. The efficiency of each technique in relation to certain sampling and output strategies is also discussed.     

Environmental and spatial controls of biotic assemblages in a discrete semi‐terrestrial habitat: comparison of organisms with different dispersal abilities sampled in the same plots

Aim The development of metacommunity theory inspired a series of studies exploring the importance of environmental and spatial effects on the composition of biotic assemblages. However, the comparison of different groups of organisms has been hampered by differences in sampling design, spatial scales or the environmental variables involved. Our aim was to test how dispersal ability affects metacommunity structure and associated species distributions by sampling different species groups in the same plots to avoid these problems. Location Western Carpathian Mountains (Europe). Methods In 191 fens we sampled the composition of diatom, bryophyte, vascular plant and mollusc assemblages, water chemistry, and macroclimatic data. We then generated spatial variables covering all relevant spatial scales using analysis of principal coordinates of neighbour matrices (PCNM). We applied the adjusted variation partitioning algorithm to quantify the effects of environment and space. Results Pure effects of water chemistry and space were highly significant for all groups of organisms. Spatial effects were stronger for groups with larger propagules (vascular plants, molluscs) than for those with smaller propagules (diatoms, bryophytes). Assemblages of macroscopic bryophytes were structured slightly less by geography and much more by environment than were those of microscopic diatoms. Vascular plant and mollusc assemblages turned out to be more spatially structured (as compared to diatom and bryophyte assemblages), with small differences between the two groups. Coarse‐scale spatial effects dominated in the bryophyte metacommunity, while in the other groups, including diatoms, finer‐scale effects were also important. Main conclusions Given that our analyses are based on a standardized sampling and analytical framework, our findings provide strong support for the hypothesis that both environmental and spatial variables structure metacommunities of organisms with very different dispersal abilities, including microscopic diatoms. In addition, we show for the first time that the strengths of these effects and their scale dependence may be predicted using important trait differences between organisms, for example differences in propagule size.     

Pellet group count methods to estimate red deer densities: Precision,potential accuracy and efficiency

Pellet group count methods are key tools for wildlife conservation and management. Therefore, it is essential to clarify the performance and suitability of the different pellet group count methods at distinct realities. This issue has been discussed by other researchers but with inconsistent conclusions, leaving open the necessity of additional studies. Aiming to fill this need, we used a combination of field data and simulation models to evaluate the density estimates, precision and potential accuracy of the results obtained through each pellet group count method, and also the sampling effort and efficiency associated with them. The methods evaluated were standing crop plot counts (FSCP), clearance plot counts (FAR), standing crop strip transect counts (ST) and standing crop line transect counts (LT). Deer density estimates by the four methods were statistically similar at all effort levels simulated. The analyses of CV trends reveal a better precision supplied by FSCP than by FAR for the same effort, while ST and LT yielded comparable values in analogous situations. The time required to make a survey is a key factor in the choice of a field technique. LT with distance sampling was the most efficient method to count pellet groups, while FAR seems to be the less proficient method. Attending to the limitations usually inherent to field surveys, like time, technicians and budget, LT appeared more efficient than the other methods, providing great precision and accuracy in less time. Nevertheless, at high densities and pronounced habitat heterogeneity, FAR became more efficient than FSC, do not requiring the decay rates, allowing accurate estimates in a few months when applied with the proper effort and environmental conditions. This study highlights the importance of carrying out pilot studies and simulation models to evaluate the sampling design prior to its implementation in the field.     

Evaluating prognostic accuracy of biomarkers in nested case-control studies

Nested case-control (NCC) design is used frequently in epidemiological studies as a cost-effective subcohort sampling strategy to conduct biomarker research. Sampling strategy, on the other hoand, creates challenges for data analysis because of outcome-dependent missingness in biomarker measurements. In this paper, we propose inverse probability weighted (IPW) methods for making inference about the prognostic accuracy of a novel biomarker for predicting future events with data from NCC studies. The consistency and asymptotic normality of these estimators are derived using the empirical process theory and convergence theorems for sequences of weakly dependent random variables. Simulation and analysis using Framingham Offspring Study data suggest that the proposed methods perform well in finite samples.