Impacts of climate timescale on the stability of trait-environment relationships.

Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait-environment model predictions if timescale is not accounted for. Here, the timescale dependence of trait-environment relationships is investigated by regressing in situ trait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales. We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub-optimal climate timescales reduces the accuracy of the resulting trait-environment relationships. This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long-term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait-environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system.

Environmental filtering mediates desert ant community assembly at two spatial scales.

Understanding the mechanisms that maintain species coexistence and determine patterns of community assembly are fundamental goals of ecology. Quantifying the relationship between species traits and stress gradients is a necessary step to disentangle assembly processes and to be able to predict the outcome of environmental change. We examined the hypothesis that desert ant communities are assembled by niche-based processes i.e., environmental filtering and limiting similarity. First, we used population-level morphological trait measurements to study the functional structure of ant communities along a dryland environmental stress gradient. Second, we developed species distribution models for each species to quantify large-scale climatic niche overlap between species. Body, femur, antennal scape, and head lengths were correlated with environmental gradients. Regionally, the ant community was significantly and functionally overdispersed in terms of morphological traits which suggests the importance of competition to ant community structure. Ant community assembly was also strongly influenced by environmental factors as the degree of functional trait divergence, but not phylogenetic divergence, decreased with increasing environmental stress. Thus, environmental stress likely mediates limiting similarity in these desert ecosystems. Species with lower climatic niche overlap were more dissimilar in morphological traits. This suggests that environmental filtering on ant functional traits is important at the scale of species distributions in addition to regional scales. This study shows that environmental and biotic filtering (i.e., niche-based assembly mechanisms) are jointly and non-independently structuring the ant community.

Extending the CWM approach to intraspecific trait variation: how to deal with overly optimistic standard tests?

Community weighted means (CWMs) are widely used to study the relationship between community-level functional traits and environment. For certain null hypotheses, CWM-environment relationships assessed by linear regression or ANOVA and tested by standard parametric tests are prone to inflated Type I error rates. Previous research has found that this problem can be solved by permutation tests (i.e., the max test). A recent extension of the CWM approach allows the inclusion of intraspecific trait variation (ITV) by the separate calculation of fixed, site-specific, and intraspecific CWMs. The question is whether the same Type I error rate inflation exists for the relationship between environment and site-specific or intraspecific CWM. Using simulated and real-world community datasets, we show that site-specific CWM-environment relationships have also inflated Type I error rate, and this rate is negatively related to the relative ITV magnitude. In contrast, for intraspecific CWM-environment relationships, standard parametric tests have the correct Type I error rate, although somewhat reduced statistical power. We introduce an ITV-extended version of the max test, which can solve the inflation problem for site-specific CWM-environment relationships and, without considering ITV, becomes equivalent to the "original" max test used for the CWM approach. We show that this new ITV-extended max test works well across the full possible magnitude of ITV on both simulated and real-world data. Most real datasets probably do not have intraspecific trait variation large enough to alleviate the problem of inflated Type I error rate, and published studies possibly report overly optimistic significance results.

Global net biome CO2 exchange predicted comparably well using parameter-environment relationships and plant functional types.

Accurate estimation and forecasts of net biome CO2 exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. Prior efforts to improve NBE predictions have predominantly focused on increasing models' structural realism (and thus complexity), but parametric error and uncertainty are also key determinants of model skill. Here, we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe, pitting the traditional plant functional type (PFT)-based approach against a novel top-down, machine learning-based "environmental filtering" (EF) approach. To do so, we simulate these contrasting methods for parameter assignment within a flexible model-data fusion framework of the terrestrial carbon cycle (CARDAMOM) at a global scale. In the PFT-based approach, model parameters from a small number of select locations are applied uniformly within regions sharing similar land cover characteristics. In the EF-based approach, a pixel's parameters are predicted based on underlying relationships with climate, soil, and canopy properties. To isolate the role of parametric from structural uncertainty in our analysis, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from CARDAMOM's Bayesian optimization approach (whereby "true" parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). When considering the mean absolute error of NBE predictions across time, we find that the EF-based approach matches or outperforms the PFT-based approach at 55% of pixels-a narrow majority. However, NBE estimates from the EF-based approach are susceptible to compensation between errors in component flux predictions and predicted parameters can align poorly with the assumed "true" values. Overall, though, the EF-based approach is comparable to conventional approaches and merits further investigation to better understand and resolve these limitations. This work provides insight into the relationship between terrestrial biosphere model performance and parametric uncertainty, informing efforts to improve model parameterization via PFT-free and trait-based approaches.

Functional diversity does not explain the co-occurrence of non-native species within a flow-modified African river system.

Globally, there is growing concern on the occurrence of multiple non-native species within invaded habitats. Proliferation of multiple non-native species together with anthropogenic-driven habitat modifications raise questions on the mechanisms facilitating the co-occurrence of these species and their potential impact within the recipient systems. Using the Great Fish River system (South Africa) which is anthropogenically-modified by inter-basin water transfer (IBWT), as a case study, this research employed trait-based approaches to explore patterns associated with the co-occurrence of multiple non-native fish species. This was achieved by investigating the role of functional diversity of non-native and native fishes in relation to their composition, distribution and environmental relationships. Nineteen functional traits that defined two broad ecological attributes (habitat use and feeding) were determined for 13 fish species that comprised eight native and five non-native fishes. We used these data to, firstly, evaluate functional diversity patterns and to compare functional traits of native and non-native fishes in the Great Fish River system. Secondly, we employed multivariate ordination analyses (factor analysis, RLQ and fourth-corner analyses) to investigate interspecific trait variations and potential species-trait-environmental relationships. From a functional diversity perspective, there were no significant differences in most functional diversity indices between native and non-native species. Despite interspecific variation in body morphology-related traits, we also found no clear separation between native and non-native species based on the ordination analysis of the functional traits. Furthermore, while RLQ ordination showed broad spatial patterns, the fourth-corner analyses revealed no significant relationships among species distribution, functional traits and environmental variables. The weak species-trait-environment relationship observed in this study suggests that environmental filtering was likely a poor determinant of functional trait structure within the Great Fish River. Modification of the natural flow regime may have weakened the relationship between species traits and the environment as has been shown in other systems.

Assessing the reliability of predicted plant trait distributions at the global scale.

AIM: Predictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant traits at the global scale and present a systematic evaluation of their reliability in terms of the accuracy of the models, ecological realism and various sources of uncertainty. LOCATION: Global. TIME PERIOD: Present. MAJOR TAXA STUDIED: Vascular plants. METHODS: We predicted global distributions of community mean specific leaf area, leaf nitrogen concentration, plant height and wood density with an ensemble modelling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the uncertainty across geographical space attributed to spatial extrapolation and diverging model predictions. RESULTS: Ensemble predictions of community mean plant height, specific leaf area and wood density resulted in ecologically plausible trait-environment relationships and trait-trait combinations. Leaf nitrogen concentration, however, could not be predicted reliably. The ensemble approach was better at predicting community trait means than any of the individual modelling techniques, which varied greatly in predictive performance and led to divergent predictions, mostly in African deserts and the Arctic, where predictions were also extrapolated. High data quality (i.e., including intraspecific variability and a representative species sample) increased model performance by 28%. MAIN CONCLUSIONS: Plant community traits can be predicted reliably at the global scale when using an ensemble approach and high-quality data for traits that mostly respond to large-scale environmental factors. We recommend applying ensemble forecasting to account for model uncertainty, using representative trait data, and more routinely assessing the reliability of trait predictions.

A fully traits-based approach to modeling global vegetation distribution.

Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs.

Inclusion of ecologically based trait variation in plant functional types reduces the projected land carbon sink in an earth system model.

Earth system models demonstrate large uncertainty in projected changes in terrestrial carbon budgets. The lack of inclusion of adaptive responses of vegetation communities to the environment has been suggested to hamper the ability of modeled vegetation to adequately respond to environmental change. In this study, variation in functional responses of vegetation has been added to an earth system model (ESM) based on ecological principles. The restriction of viable mean trait values of vegetation communities by the environment, called 'habitat filtering', is an important ecological assembly rule and allows for determination of global scale trait-environment relationships. These relationships were applied to model trait variation for different plant functional types (PFTs). For three leaf traits (specific leaf area, maximum carboxylation rate at 25 °C, and maximum electron transport rate at 25 °C), relationships with multiple environmental drivers, such as precipitation, temperature, radiation, and CO2 , were determined for the PFTs within the Max Planck Institute ESM. With these relationships, spatiotemporal variation in these formerly fixed traits in PFTs was modeled in global change projections (IPCC RCP8.5 scenario). Inclusion of this environment-driven trait variation resulted in a strong reduction of the global carbon sink by at least 33% (2.1 Pg C yr(-1) ) from the 2nd quarter of the 21st century onward compared to the default model with fixed traits. In addition, the mid- and high latitudes became a stronger carbon sink and the tropics a stronger carbon source, caused by trait-induced differences in productivity and relative respirational costs. These results point toward a reduction of the global carbon sink when including a more realistic representation of functional vegetation responses, implying more carbon will stay airborne, which could fuel further climate change.

Leaf morphological traits as adaptations to multiple climate gradients.

Leaf morphological traits vary systematically along climatic gradients. However, recent studies in plant functional ecology have mainly analysed quantitative traits, while numerical models of species distributions and vegetation function have focused on traits associated with resource acquisition; both ignore the wider functional significance of leaf morphology.A dataset comprising 22 leaf morphological traits for 662 woody species from 92 sites, representing all biomes present in China, was subjected to multivariate analysis in order to identify leading dimensions of trait covariation (correspondence analysis), quantify climatic and phylogenetic contributions (canonical correspondence analysis with variation partitioning) and characterise co-occurring trait syndromes (k-means clustering) and their climatic preferences.Three axes accounted for >20% of trait variation in both evergreen and deciduous species. Moisture index, precipitation seasonality and growing-season temperature explained 8%-10% of trait variation; family 15%-32%. Microphyll or larger, mid- to dark green leaves with drip tips in wetter climates contrasted with nanophyll or smaller glaucous leaves without drip tips in drier climates. Thick, entire leaves in less seasonal climates contrasted with thin, marginal dissected, aromatic and involute/revolute leaves in more seasonal climates. Thick, involute, hairy leaves in colder climates contrasted with thin leaves with marked surface structures (surface patterning) in warmer climates. Distinctive trait clusters were linked to the driest and most seasonal climates, for example the clustering of picophyll, fleshy and succulent leaves in the driest climates and leptophyll, linear, dissected, revolute or involute and aromatic leaves in regions with highly seasonal rainfall. Several trait clusters co-occurred in wetter climates, including clusters characterised by microphyll, moderately thick, patent and entire leaves or notophyll, waxy, dark green leaves. Synthesis. The plastic response of size, shape, colour and other leaf morphological traits to climate is muted, thus their apparent shift along climate gradients reflects plant adaptations to environment at a community level as determined by species replacement. Information on leaf morphological traits, widely available in floras, could be used to strengthen predictive models of species distribution and vegetation function.

Comparison of environmental inference approaches for ecometric analyses: Using hypsodonty to estimate precipitation.

Ecometrics is the study of community-level functional trait-environment relationships. We use ecometric analyses to estimate paleoenvironment and to investigate community-level functional changes through time.We evaluate four methods that have been used or have the potential to be used in ecometric analyses for estimating paleoenvironment to determine whether there have been systematic differences in paleoenvironmental estimation due to choice of the estimation method. Specifically, we evaluated linear regression, polynomial regression, nearest neighbor, and maximum-likelihood methods to explore the predictive ability of the relationship for a well-known ecometric dataset of mammalian herbivore hypsodonty metrics (molar tooth crown to root height ratio) and annual precipitation. Each method was applied to 43 Pleistocene fossil sites and compared to annual precipitation from global climate models. Sites were categorized as glacial or interglacial, and paleoprecipitation estimates were compared to the appropriate model.Estimation methods produce results that are highly correlated with log precipitation and estimates from the other methods (p < 0.001). Differences between estimated precipitation and observed precipitation are not significantly different across the four methods, but maximum likelihood produces the most accurate estimates of precipitation. When applied to paleontological sites, paleoprecipitation estimates align more closely with glacial global climate models than with interglacial models regardless of the age of the site.Each method has constraints that are important to consider when designing ecometric analyses to avoid misinterpretations when ecometric relationships are applied to the paleontological record. We show interglacial fauna estimates of paleoprecipitation more closely match glacial global climate models. This is likely because of the anthropogenic effects on community reassembly in the Holocene.

Double constrained ordination for assessing biological trait responses to multiple stressors: A case study with benthic macroinvertebrate communities.

Benthic macroinvertebrate communities are used as indicators for anthropogenic stress in freshwater ecosystems. To better understand the relationship between anthropogenic stress and changes in macroinvertebrate community composition, it is important to understand how different stressors and species traits are associated, and how these associations influence variation in species occurrence and abundances. Here, we show the capacity of the multivariate technique of double constrained correspondence analysis (dc-CA) to analyse trait-environment relationships, and we compare it with the redundancy analysis method on community weighted mean values of traits (CWM-RDA), which is frequently used for this type of analysis. The analyses were based on available biomonitoring data for macroinvertebrate communities from the Danube River. Results from forward selection of traits and environmental variables using dc-CA analyses showed that aquatic stages, reproduction techniques, dispersal tactics, locomotion and substrate relations, altitude, longitudinal and transversal distribution, and substrate preferendum were significantly related to habitat characteristics, hydromorphological alterations and water quality measurements such as physico-chemical parameters, heavy metals, pesticides and pharmaceuticals. Environmental variables significantly associated with traits using the CWM-RDA method were generally consistent with those found in dc-CA analysis. However, the CWM-RDA does neither test nor explicitly select traits, while dc-CA tests and selects both traits and environmental variables. Moreover, the dc-CA analysis revealed that the set of environmental variables was much better in explaining the community data than the available trait set, a kind of information that can neither be obtained from CWM-RDA nor from RLQ (Environment, Link and Trait data), which is a close cousin of dc-CA but not regression-based. Our results suggest that trait-based analysis based on dc-CA may be useful to assess mechanistic links between multiple anthropogenic stressors and ecosystem health, but more data sets should be analysed in the same manner.

Temperature and stoichiometric dependence of phytoplankton traits.

Understanding the links between intraspecific trait variability and environmental gradients is an important step toward unravelling the mechanisms that link species performance to environmental variation. Here, we performed a comparative, experimental study to investigate variability of cellular traits in three prokaryotic and three eukaryotic freshwater phytoplankton species along gradients of temperature and nitrogen:phosphorus ratio (N:P) in laboratory microcosms. Temperature and N:P strongly affect phytoplankton growth and are changing due to climate change and eutrophication. Metabolic theory and allometric scaling predict that smaller organisms should be favored at higher temperatures through improved metabolic uptake partly due to greater surface area to volume ratios. In addition, chlorophyll a (chl a) concentration should increase due to higher chlorophyll synthesis in response to light limitation at higher cell densities. We found that cell volume both increased and decreased with temperature, whereas intermediate N:P yielded higher growth rates and more extreme conditions yielded bigger cell volumes. Species growth responses to these gradients were distinct and not related to phylogenetic differences. Meaningfully coupled traits like the chl a fluorescence and cell volume shifted consistently and can improve our understanding of individual cell responses to abiotic drivers. This study showed that intraspecific trait variability of freshwater phytoplankton harbors potential for short term acclimation to environmental gradients. Finally, the high trait variability in some species has strong implications for their ecology and the accuracy of predictions where responses may differ when based on mean or fixed trait values.

Reproductive traits associated with species turnover of amphibians in Amazonia and its Andean slopes.

Assembly of ecological communities is important for the conservation of ecosystems, predicting perturbation impacts, and understanding the origin and loss of biodiversity. We tested how amphibian communities are assembled by neutral and niche-based mechanisms, such as habitat filtering. Species richness, ß-diversities, and reproductive traits of amphibians were evaluated at local scale in seven habitats at different elevation and disturbance levels in Wisui Biological Station, Morona-Santiago, Ecuador, on the foothills of the Cordillera del Kutukú; and at regional scale using 109 localities across evergreen forests of Amazonia and its Andean slopes (0-3,900 m a.s.l.). At local scale, species composition showed strong differences among habitats, explained mainly by turnover. Reproductive modes occurred differently across habitats (e.g., prevalence of direct developers at high elevation, where breeding in ground level water disappears). At regional scale, elevation was the most important factor explaining the changes in species richness, reproductive trait occurrences, and biotic dissimilarities. Species number in all groups decreased with elevation except for those with lotic tadpoles and terrestrial reproduction stages. Seasonality, annual precipitation, and relative humidity partially explained the occurrence of some reproductive traits. Biotic dissimilarities were also mostly caused by turnover rather than nestedness and were particularly high in montane and foothill sites. Within lowlands, geographic distance explained more variability than elevation. Habitat filtering was supported by the different occurrence of reproductive traits according to elevation, water availability, and breeding microhabitats at both scales, as well as other assembly mechanisms based in biotic interactions at local scale. Human-generated land use changes in Amazonia and its Andean slopes reduce local amphibian biodiversity by alteration of primary forests and loss of their microhabitats and the interaction network that maintains their unique amphibian assemblages with different reproductive strategies.

Size and Reproductive Traits Rather than Leaf Economic Traits Explain Plant-Community Composition in Species-Rich Annual Vegetation along a Gradient of Land Use Intensity.

Agricultural land use imposes a major disturbance on ecosystems worldwide, thus greatly modifying the taxonomic and functional composition of plant communities. However, mechanisms of community assembly, as assessed by plant functional traits, are not well known for dryland ecosystems under agricultural disturbance. Here we investigated trait responses to disturbance intensity and availability of resources to identify the main drivers of changes in composition of semiarid communities under diverging land use intensities. The eastern Mediterranean study region is characterized by an extended rainless season and by very diverse, mostly annual communities. At 24 truly replicated sites, we recorded the frequency of 241 species and the functional traits of the 53 most common species, together with soil resources and disturbance intensity across a land use gradient ranging from ungrazed shrubland to intensively managed cropland (six land use types). Multivariate RLQ analysis (linking functional traits, sites and environmental factors in a three-way ordination) and fourth corner analysis (revealing significant relations between traits and environmental factors) were used in a complementary way to get insights into trait-environment relations. Results revealed that traits related to plant size (reflecting light absorption and competitive ability) increased with resource availability, such as soil phosphorus and water holding capacity. Leaf economic traits, such as specific leaf area (SLA), leaf nitrogen content (LNC), and leaf dry matter content showed low variation across the disturbance gradient and were not related to environmental variables. In these herbaceous annual communities where plants grow and persist for just 3-5 months, SLA and LNC were unrelated, which together with relatively high SLA values might point to strategies of drought escape and grazing avoidance. Seed mass was high both at higher and lower resource availability, whereas seed number increased with the degree of disturbance. The strong response of size and reproduction traits, and the missing response of leaf economic traits reveal light interception and resource competition rather than resource acquisition and litter decomposition as drivers of plant community composition. Deviations from trait relationships observed in commonly studied temperate ecosystems confirm that climatic conditions play a fundamental role by filtering species with particular life forms and ecological strategies.