Species traits are associated with the quality of bioclimatic models

Aim To analyse the effects of nine species trait variables on the accuracy of bioclimatic envelope models built for 98 butterfly species. Location Finland, northern Europe. Methods Data from a national butterfly atlas monitoring scheme (NAFI) collected from 1991–2003 with a resolution of 10 × 10 km were used in the analyses. Generalized additive models (GAMs) were constructed for 98 butterfly species to predict their occurrence as a function of climatic variables. Modelling accuracy was measured as the cross‐validation area under the curve (AUC) of the receiver–operating characteristic plot. Observed variation in modelling accuracy was related to species traits using multiple GAMs. The effects of phylogenetic relatedness among butterflies were accounted for by using generalized estimation equations. Results The values of the cross‐validation AUC for the 98 species varied between 0.56 and 1.00 with a mean of 0.79. Five species trait variables were included in the GAM that explained 71.4% of the observed variation in modelling accuracy. Four variables remained significant after accounting for phylogenetic relatedness. Species with high mobility and a long flight period were modelled less accurately than species with low mobility and a short flight period. Large species (>50 mm in wing span) were modelled more accurately than small ones. Species inhabiting mires had especially poor models, whereas the models for species inhabiting rocky outcrops, field verges and open fells were more accurate compared with other habitats. Main conclusions These results draw attention to the importance of species traits variables for species–climate impact models. Most importantly, species traits may have a strong impact on the performance of bioclimatic envelope models, and certain trait groups can be inherently difficult to model reliably. These uncertainties should be taken into account by downweighting or excluding species with such traits in studies applying bioclimatic modelling and making assessments of the impacts of climate change.     

Ecophysiological and bioclimatic foundations for a global plant functional classification

Question: What plant properties might define plant functional types (PFTs) for the analysis of global vegetation responses to climate change, and what aspects of the physical environment might be expected to predict the distributions of PFTs? Methods: We review principles to explain the distribution of key plant traits as a function of bioclimatic variables. We focus on those whole‐plant and leaf traits that are commonly used to define biomes and PFTs in global maps and models. Results: Raunkiær's plant life forms (underlying most later classifications) describe different adaptive strategies for surviving low temperature or drought, while satisfying requirements for reproduction and growth. Simple conceptual models and published observations are used to quantify the adaptive significance of leaf size for temperature regulation, leaf consistency for maintaining transpiration under drought, and phenology for the optimization of annual carbon balance. A new compilation of experimental data supports the functional definition of tropical, warm‐temperate, temperate and boreal phanerophytes based on mechanisms for withstanding low temperature extremes. Chilling requirements are less well quantified, but are a necessary adjunct to cold tolerance. Functional traits generally confer both advantages and restrictions; the existence of trade‐offs contributes to the diversity of plants along bioclimatic gradients. Conclusions: Quantitative analysis of plant trait distributions against bioclimatic variables is becoming possible; this opens up new opportunities for PFT classification. A PFT classification based on bioclimatic responses will need to be enhanced by information on traits related to competition, successional dynamics and disturbance.     

Phylogenetic signatures of adaptation: The Arabis hirsuta species aggregate (Brassicaceae) revisited

The mustard family, Brassicaceae, is well-known for its homoplasy in almost any morphological character at practically all taxonomic levels. The genus Arabis, within the largest tribe of the Brassicaceae, is such an example comprising numerous para- and polyphyletic groups of taxa. Past research during the last 15 years has unraveled many phylogenetic relationships among the ∼550 (or more) species within the notoriously difficult tribe Arabideae. The European Arabis hirsuta species aggregate has remained unexplored, however. Herein we analyze phylogenetic relationships using nuclear ITS and plastid DNA sequences of Eurasian Arabis to characterize Hairy rock cress (A. hirsuta) and its relatives. Representative geographic sampling is used to study character and trait evolution, and bioclimatic data are used to differentiate between species. Our overview puts European Arabis into a reliable evolutionary framework, and we provide some striking insights into evolutionary trends and correlating morphological characters from seeds and flowers with environmental data such as climate variables and elevation. We demonstrate independent parallel evolution of sets of traits, and, therefore, we could further elaborate our previous findings that within tribe Arabideae high speciation rates are correlated with perennial growth form and occurrence at higher elevation. Finally some taxonomical remarks are provided to give added context.     

Leaf trait-environment relationships in a subtropical broadleaved forest in South-East China

Although trait analyses have become more important in community ecology, trait-environment correlations have rarely been studied along successional gradients. We asked which environmental variables had the strongest impact on intraspecific and interspecific trait variation in the community and which traits were most responsive to the environment. We established a series of plots in a secondary forest in the Chinese subtropics, stratified by successional stages that were defined by the time elapsed since the last logging activities. On a total of 27 plots all woody plants were recorded and a set of individuals of every species was analysed for leaf traits, resulting in a trait matrix of 26 leaf traits for 122 species. A Fourth Corner Analysis revealed that the mean values of many leaf traits were tightly related to the successional gradient. Most shifts in traits followed the leaf economics spectrum with decreasing specific leaf area and leaf nutrient contents with successional time. Beside succession, few additional environmental variables resulted in significant trait relationships, such as soil moisture and soil C and N content as well as topographical variables. Not all traits were related to the leaf economics spectrum, and thus, to the successional gradient, such as stomata size and density. By comparing different permutation models in the Fourth Corner Analysis, we found that the trait-environment link was based more on the association of species with the environment than of the communities with species traits. The strong species-environment association was brought about by a clear gradient in species composition along the succession series, while communities were not well differentiated in mean trait composition. In contrast, intraspecific trait variation did not show close environmental relationships. The study confirmed the role of environmental trait filtering in subtropical forests, with traits associated with the leaf economics spectrum being the most responsive ones.     

Large Scale Relationship between Aquatic Insect Traits and Climate

Climate is the predominant environmental driver of freshwater assemblage pattern on large spatial scales, and traits of freshwater organisms have shown considerable potential to identify impacts of climate change. Although several studies suggest traits that may indicate vulnerability to climate change, the empirical relationship between freshwater assemblage trait composition and climate has been rarely examined on large scales. We compared the responses of the assumed climate-associated traits from six grouping features to 35 bioclimatic indices (~18 km resolution) for five insect orders (Diptera, Ephemeroptera, Odonata, Plecoptera and Trichoptera), evaluated their potential for changing distribution pattern under future climate change and identified the most influential bioclimatic indices. The data comprised 782 species and 395 genera sampled in 4,752 stream sites during 2006 and 2007 in Germany (~357,000 km^² spatial extent). We quantified the variability and spatial autocorrelation in the traits and orders that are associated with the combined and individual bioclimatic indices. Traits of temperature preference grouping feature that are the products of several other underlying climate-associated traits, and the insect order Ephemeroptera exhibited the strongest response to the bioclimatic indices as well as the highest potential for changing distribution pattern. Regarding individual traits, insects in general and ephemeropterans preferring very cold temperature showed the highest response, and the insects preferring cold and trichopterans preferring moderate temperature showed the highest potential for changing distribution. We showed that the seasonal radiation and moisture are the most influential bioclimatic aspects, and thus changes in these aspects may affect the most responsive traits and orders and drive a change in their spatial distribution pattern. Our findings support the development of trait-based metrics to predict and detect climate-related changes of freshwater assemblages.     

Incorporating Field Studies into Species Distribution and Climate Change Modelling: A Case Study of the Koomal Trichosurus vulpecula hypoleucus (Phalangeridae)

Species distribution models (SDMs) are an effective way of predicting the potential distribution of species and their response to environmental change. Most SDMs apply presence data to a relatively generic set of predictive variables such as climate. However, this weakens the modelling process by overlooking the responses to more cryptic predictive variables. In this paper we demonstrate a means by which data gathered from an intensive animal trapping study can be used to enhance SDMs by combining field data with bioclimatic modelling techniques to determine the future potential distribution for the koomal (Trichosurus vulpecula hypoleucus). The koomal is a geographically isolated subspecies of the common brushtail possum, endemic to south-western Australia. Since European settlement this taxon has undergone a significant reduction in distribution due to its vulnerability to habitat fragmentation, introduced predators and tree/shrub dieback caused by a virulent group of plant pathogens of the genus Phytophthora. An intensive field study found: 1) the home range for the koomal rarely exceeded 1 km in in length at its widest point; 2) areas heavily infested with dieback were not occupied; 3) gap crossing between patches (>400 m) was common behaviour; 4) koomal presence was linked to the extent of suitable vegetation; and 5) where the needs of koomal were met, populations in fragments were demographically similar to those found in contiguous landscapes. We used this information to resolve a more accurate SDM for the koomal than that created from bioclimatic data alone. Specifically, we refined spatial coverages of remnant vegetation and dieback, to develop a set of variables that we combined with selected bioclimatic variables to construct models. We conclude that the utility value of an SDM can be enhanced and given greater resolution by identifying variables that reflect observed, species-specific responses to landscape parameters and incorporating these responses into the model.     

Integrating life history traits into predictive phylogeography

Predictive phylogeography seeks to aggregate genetic, environmental and taxonomic data from multiple species in order to make predictions about unsampled taxa using machine‐learning techniques such as Random Forests. To date, organismal trait data have infrequently been incorporated into predictive frameworks due to difficulties inherent to the scoring of trait data across a taxonomically broad set of taxa. We refine predictive frameworks from two North American systems, the inland temperate rainforests of the Pacific Northwest and the Southwestern Arid Lands (SWAL), by incorporating a number of organismal trait variables. Our results indicate that incorporating life history traits as predictor variables improves the performance of the supervised machine‐learning approach to predictive phylogeography, especially for the SWAL system, in which predictions made from only taxonomic and climate variables meets only moderate success. In particular, traits related to reproduction (e.g., reproductive mode; clutch size) and trophic level appear to be particularly informative to the predictive framework. Predictive frameworks offer an important mechanism for integration of organismal trait, environmental data, and genetic data in phylogeographic studies.     

Can CSR classification be generally applied outside Britain?

The general applicability of CSR classification (a methodology for applying CSR theory) has not been tested outside Britain. We hypothesised that principal axes of trait variation for plant species native to southern European continental, sub-alpine and alpine bioclimatic zones correlate with CSR classification scores. Functional traits were quantified in situ for 506 species of 57 families. Multivariate analysis revealed three main modes in which plants invested biomass: (1) architectural extension of individual ramets (tall canopies, large leaves), (2) durable tissues (high leaf dry matter contents and carbon contents, low specific leaf areas) or (3) regenerative development (early, extensive flowering with delicate, nitrogen-rich leaves). These were highly significantly correlated with the extent of competitive ability (C), stress-tolerance (S) and ruderalism (R), respectively, determined by CSR classification. Despite being calibrated using only 43 British species, CSR classification is consistent with trait variation in a phylogenetically broader sample of species native to a wider range of bioclimatic zones.     

Genetic linkage analysis of a dichotomous trait incorporating a tightly linked quantitative trait in affected sib pairs

Many complex diseases are usually considered as dichotomous traits but are also associated with quantitative biological markers or quantitative risk factors. For such dichotomous traits, although their associated quantitative traits may not directly underly the diagnosis of the disease status, if the associated quantitative trait is also linked to the chromosomal regions linked to the dichotomous trait, then joint analysis of dichotomous and quantitative traits should be more efficient than consideration of them separately. Previous studies have focused on the situation when a dichotomous trait can be modeled by a threshold process acting on a single underlying normal liability distribution. However, for many complex disorders, including most psychiatric disorders, diagnosis is generally based on a set of binary or discrete criteria. These traits cannot be modeled on the basis of a threshold process acting on an underlying continuous trait. We propose a likelihood-based method that efficiently combines such a discrete trait and an associated quantitative trait in the analysis, using affected-sib-pair data. Our simulation studies suggest that joint analysis increases the power to detect linkage of dichotomous traits. We also apply the proposed new method to an asthma genome-scan data set and incorporate the total serum immunoglobulin E level in the analysis.     

Consistency statistics and genetic parameters for taste panel assessed meat quality traits and their relationship with carcass quality traits in a commercial population of Angus-sired beef cattle

Sensory traits, such as juiciness and tenderness, are known to be important to the consumer and thus will influence their consumption of meat, specifically beef. These traits are difficult to measure and often require the use of taste panels to assess the complex parameters involved in the eating experience. Such panels are potentially a large source of measurement error, which may reduce the effectiveness of breeding programmes based on the data they generate. The aim of this study was to assess the quality of such taste panel-derived sensory traits as well as calculating genetic parameters and residual correlations for these traits along with a further set of traditional carcass quality traits. The study examined a sample of 443 Aberdeen Angus-cross animals collected from 14 breeder-finisher farms throughout Scotland. To assess the quality of the taste panel measurements, three consistency statistics were calculated: (i) panel-member consistency, i.e. the extent to which an individual panel member varied in their scoring for a given trait over the period of the experiment; (ii) repeatability, i.e. the consistency with which an individual panel member was able to score a trait on repeated samples from the same animal; and (iii) reproducibility, i.e. the extent to which taste panel members agreed with each other when scoring a trait. These consistency statistics were moderately high, particularly for panel-member consistency and reproducibility, with values ranging from 0.48 to 0.81 and 0.43 to 0.73 respectively. Estimated heritabilities were low for most of the sensory taste-panel-evaluated traits where the maximum value was 0.16 for overall liking. Residual correlations were high between many of the closely related sensory traits, although few significant correlations were found between the carcass quality data and meat quality traits.     

Functional flower traits and their diversity drive pollinator visitation

Recent studies have shown that the diversity of flowering plants can enhance pollinator richness and visitation frequency and thereby increase the resilience of pollination. It is assumed that flower traits explain these effects, but it is still unclear which flower traits are responsible, and knowing that, if pollinator richness and visitation frequency are more driven by mass‐ratio effects (mean trait values) or by trait diversity. Here, we analyse a three‐year data set of pollinator observations collected in a European grassland plant diversity experiment (The Jena experiment). The data entail comprehensive flower trait measurements, including reward traits (nectar and pollen amount), morphological traits (height, symmetry, area, colour spectra) and chemical traits (nectar‐amino acid and nectar‐sugar concentration). We test if pollinator species richness and visitation frequency of flower communities depend on overall functional diversity combining all flower traits within a community, single trait diversities (within trait variation) and community‐weighted means of the single traits, using Bayesian inference. Overall functional diversity did not affect pollinator species richness, but reduced visitation frequency. When looking at individual flower traits separately, we found that single trait diversity of flower reflectance and flower morphology were important predictors of pollinator visitation frequency. Moreover, independent of total flower abundance, community‐weighted means of flower height, area, reflectance, nectar‐sugar concentration and nectar‐amino acid concentration strongly affected both pollinator species richness and visitation frequency. Our results, challenge the idea that functional diversity always positively affects ecosystem functions. Nonetheless, we demonstrate that both single trait diversity and mass‐ratio effects of flower traits play an important role for diverse and frequent flower visits, which underlines the functionality of flower traits for pollination services.     

Patterns of climber distribution in the temperate forests of the Americas

Aims and Methods We propose a standard protocol at the landscape to continental scale for examining to what extent the range of ecological conditions found in temperate latitudes explains the variations in climber species richness and traits. The protocol was tested in forests of the two Americas. The data set included 151 climber species. We selected four categorical traits and grouped these species into six clusters with regard to these traits. Floristic records of American forests were first gathered into alliances, second combined with bioclimatic indices (rainfall, temperature, continentality). We obtained a total of 59 vegetational units in which we calculated values of climber species richness and proportion of clusters. Vegetational units were ultimately gathered into five forest formations (characterized by leaf longevity). Wetlands and uplands were considered separately.Important findings Our results emphasize clear trends in large-scale patterns of climber distribution, independently of taxonomy. Climber species richness (in particular woody climbers) peaks in moist and warm upland forests with oceanic climates, and where conifers are rare. In flooded areas, climber richness is also very high and peaks in seasonally flooded large floodplains. In ecological conditions of frost, dryness or lack of nutrients, climber species richness, abundance and trait diversity decline, resulting in the dominance of small, twining and deciduous life traits.     

Plant functional identity as the predictor of carbon storage in semi-arid ecosystems

Background: There is an increasing consensus that ecosystem processes are governed by functional identity and trait variation rather than species richness. Despite its importance, the relative effect of relevant functional traits for carbon storage has remained mostly untested in different bioclimatic regions.

Aims: In this study, different components of functional diversity such as community-weighted means of trait values (CWM), functional trait diversity (Rao’s quadratic diversity), functional richness (FRi), functional evenness (FEv) and functional divergence (FDiv) were used to associate carbon content of above-ground biomass, litter and soil in four bioclimatic regions including warm and cold-steppe, semi-steppe rangelands and oak dry forest in the south-west of Iran.

Methods: Several key important traits highly associated with carbon storage including specific leaf area (SLA), height (H), leaf dry matter content, leaf nitrogen and phosphorus content (LNC and LPC), leaf longevity, wood specific gravity and life form were determined to quantify single and multiple traits that contribute to different components of plant functional diversity.

Results: The results showed that CWM of H, Chamaephyte life form, LNC and LPC were among the most important aspects of functional diversity that positively predicted carbon storage in above-ground biomass and soil. We also observed the negative association of carbon storage with FEv of LNC, Rao of LNC and FEv of multiple traits in the rangelands and the negative association of carbon storage with FDiv of SLA in the forest.

Conclusions: Our results indicate that different components of functional diversity are essential for a mechanistic understanding of the role of plant diversity for carbon storage. The negative associations between FDiv and FEv and carbon storage do not provide support for the complementarity niche hypothesis. Our results suggest that in the more functionally diverse ecosystems dominated by functionally important species with key traits, the so-called functional identity does indeed promote carbon storage, at least in these semi-arid ecosystems.     

Invertebrate traits for the biomonitoring of large European rivers: an assessment of specific types of human impact

1. The power of selected biological invertebrate traits for discriminating different types of human impact (heavy metal pollution and cargo‐ship traffic) were tested using ecological reasoning and linear Discriminant Function Analysis (DFA). 2. Frequency distributions of individual traits and categories of traits from 68 least impacted river reaches (LIRRs) and 304 impacted river reaches were used to define simple assessment rules based on ecological reasoning for specific impairments in large European rivers. In calibration, a maximum of three variables with a priori predictions and two different impairment threshold levels were used. Similarly, DFA was performed on the same variables included in the ecological reasoning approach, but also on all available traits or trait categories. 3. Validation with an independent data set (40 LIRRs, 291 variously impacted river reaches) and using the ecological reasoning approach showed that 75–78% of the reaches were correctly assign with rules on all impact types, 35–57% with rules on heavy metal pollution and 78–93% with rules on cargo‐ship traffic. By comparison, validation showed that DFA performed globally poorer than the ecological reasoning approach. In addition, the performance of the rules based on ecological reasoning remained stable, whereas DFA performance changed between calibration and validation. 4. Although not defined for this purpose, our study provided alarming evidence regarding the impact of cargo‐ship traffic on invertebrate communities in river reaches. Reaches with cargo‐ship traffic were found to have more genera with long life cycles that reproduce repeatedly by ovoviviparity and have a sessile life. 5. The performance of our trait‐based approach to correctly assign reaches to either least impacted or impacted conditions should promote further research on the topic across larger geographic areas (without regionalization) and across smaller stream types to provide a powerful biomonitoring tool that fulfils current European Union directives.     

Mapping quantitative trait loci from a single-tail sample of the phenotype distribution including survival data

A new effective Bayesian quantitative trait locus (QTL) mapping approach for the analysis of single-tail selected samples of the phenotype distribution is presented. The approach extends the affected-only tests to single-tail sampling with quantitative traits such as the log-normal survival time or censored/selected traits. A great benefit of the approach is that it enables the utilization of multiple-QTL models, is easy to incorporate into different data designs (experimental and outbred populations), and can potentially be extended to epistatic models. In inbred lines, the method exploits the fact that the parental mating type and the linkage phases (haplotypes) are known by definition. In outbred populations, two-generation data are needed, for example, selected offspring and one of the parents (the sires) in breeding material. The idea is to statistically (computationally) generate a fully complementary, maximally dissimilar, observation for each offspring in the sample. Bayesian data augmentation is then used to sample the space of possible trait values for the pseudoobservations. The benefits of the approach are illustrated using simulated data sets and a real data set on the survival of F₂ mice following infection with Listeria monocytogenes.     

BryForTrait – A life‐history trait database of forest bryophytes

In ecological research, plant functional trait analyses are widely applied to understand to what extent the inter‐specific variation in trait attributes has an adaptive value or to predict ecosystem processes and changes. Compared to vascular plants, trait studies using bryophytes are scarce, which is likely due to missing trait information for bryophyte species. With the BryForTrait database, we want to reduce this deficit. Our database represents a compilation of autecological information and morphological and regenerative trait data on different stages of the life cycle of bryophytes occurring in forest ecosystems. The database contains information for 35 traits and 721 Central European bryophyte species; in total more than 23,000 trait values. The BryForTrait database will enable future trait studies, providing new insights into bryophyte‐dominated ecosystems.     

A Multi-Trait,Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature,Fatness and Reproduction in Beef Cattle

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t''V⁻¹t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.     

Seasonality and phenology alter functional leaf traits

In plant ecophysiology, functional leaf traits are generally not assessed in relation to phenological phase of the canopy. Leaf traits measured in deciduous perennial species are known to vary between spring and summer seasons, but there is a knowledge gap relating to the late-summer phase marked by growth cessation and bud set occurring well before fall leaf senescence. The effects of phenology on canopy physiology were tested using a common garden of over 2,000 black cottonwood (Populus trichocarpa) individuals originating from a wide geographical range (44–60ºN). Annual phenological events and 12 leaf-based functional trait measurements were collected spanning the entire summer season prior to, and following, bud set. Patterns of seasonal trait change emerged by synchronizing trees using their date of bud set. In particular, photosynthetic, mass, and N-based traits increased substantially following bud set. Most traits were significantly different between pre-bud set and post-bud set phase trees, with many traits showing at least 25 % alteration in mean value. Post-bud set, both the significance and direction of trait–trait relationships could be modified, with many relating directly to changes in leaf mass. In Populus, these dynamics in leaf traits throughout the summer season reflected a shift in whole plant physiology, but occurred long before the onset of leaf senescence. The marked shifts in measured trait values following bud set underscores the necessity to include phenology in trait-based ecological studies or large-scale phenotyping efforts, both at the local level and larger geographical scale.     

Seedling development in a Brassica napus diversity set and its relationship to agronomic performance

Brassica napus L. is the leading European oilseed crop and has therefore a high economical importance. The objectives of our study were to examine (1) the patterns of phenotypic diversity in a species-wide B. napus germplasm set of 518 inbreds with respect to various seedling development, agronomic, and seed quality traits as well as (2) the interrelationship of the examined traits and their use in selection on correlated traits. The B. napus germplasm set was evaluated in greenhouse and field trials for several seedling development, agronomic, and seed quality traits. The traits were highly correlated within the individual trait categories and moderately correlated between the different trait categories. We observed differences in phenotypic diversity among the examined eight germplasm types. The reduction of phenotypic diversity was on average more pronounced for the seedling development traits than for the agronomic and seed quality traits, suggesting that plant breeders need to introgress new genetic variation with respect to the former.