The life-history basis of latitudinal diversity gradients: how do species traits vary from the poles to the equator.

1. Latitudinal variation among species in life-history traits is often suggested to contribute to high tropical species richness. However, traditional methods of analysing such variation rarely control for phylogeny and latitudinal range overlap between species, potentially giving misleading results. 2. Using a method of pairwise independent contrasts which overcomes these problems, I tested for latitudinal variation among bird species in a number of traits which have been linked, theoretically or empirically, with both latitude and species richness. 3. This method indicates strong support for Rapoport's Rule and decreasing clutch size towards the equator in both hemispheres, but only partial support for decreasing body size and ecological generalism towards the equator. 4. Indirect measures of sexual selection (sexual dichromatism and size dimorphism) show no variation with latitude; an apparent increase in dichromatism towards the equator is shown to be an artefact of phylogeny. 5. Many of the associations between life history and latitude were not detected by traditional cross-species analyses, highlighting the importance of incorporating phylogeny and overlap in studies of geographical life-history variation. Establishing associations between life-history traits and latitude does not prove, but is a necessary prerequisite for., a link between these traits and latitudinal diversity gradients.

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.

ANALYSIS OF MACROINVERTEBRATE COMMUNITIES IN SEASONAL WETLANDS THROUGH TIME, ACROSS SPACE, AND USING SPECIES TRAITS

Restoration of natural wetlands may be informed by macroinvertebrate community composition. Macroinvertebrate communities of wetlands are influenced by environmental characteristics such as vegetation, soil, hydrology, land use, and isolation. This dissertation explores multiple approaches to the assessment of wetland macroinvertebrate community composition, and demonstrates how these approaches can provide complementary insights into the community ecology of aquatic macroinvertebrates. Specifically, this work focuses on macroinvertebrates of Delmarva Bays, isolated seasonal wetlands found on Maryland’s eastern shore. A comparison of macroinvertebrate community change over a nine years in a restored wetland complex indicated that the macroinvertebrate community of a rehabilitated wetlands more rapidly approximated the community of a reference site than did a newly created wetland. The recovery of a natural macroinvertebrate community in the rehabilitated wetland indicated that wetland rehabilitation should be prioritized over wetland creation and long-term monitoring may be needed to evaluate restoration success. This study also indicated that characteristics of wetland vegetation reflected community composition. The connection between wetland vegetation and macroinvertebrate community composition led to a regional assessment of predaceous diving beetle (Coleoptera: Dytiscidae) community composition in 20 seasonal wetlands, half with and half without sphagnum moss (Sphagnum spp.). Species-level identifications indicated that wetlands with sphagnum support unique and diverse assemblages of beetles. These patterns suggest that sphagnum wetlands provide habitat that supports biodiversity on the Delmarva Peninsula. To compare traits of co-occurring beetles, mandible morphology and temporal and spatial variation were measured between three species of predaceous diving beetles. Based on mandible architecture, all species may consume similarly sized prey, but prey characteristics likely differ in terms of piercing force required for successful capture and consumption. Therefore, different assemblages of aquatic beetles may have different effects on macroinvertebrate community structure. Integrating community-level and species-level data strengthens the association between individual organisms and their ecological role. Effective restoration of imperiled wetlands benefits from this integration, as it informs the management practices that both preserve biodiversity and promote ecosystem services.

The influence of environmental spatial structure on the life history traits and diversity of species in a metacommunity

Several models have been proposed to understand how so many species can coexist in ecosystems. Despite evidence showing that natural habitats are often patchy and fragmented, these models rarely take into account environmental spatial structure. In this study we investigated the influence of spatial structure in habitat and disturbance regime upon species' traits and species' coexistence in a metacommunity. We used a population-based model to simulate competing species in spatially explicit landscapes. The species traits we focused on were dispersal ability, competitiveness, reproductive investment and survival rate. Communities were characterized by their species richness and by the four life-history traits averaged over all the surviving species. Our results show that spatial structure and disturbance have a strong influence on the equilibrium life-history traits within a metacommunity. In the absence of disturbance, spatially structured landscapes favour species investing more in reproduction, but less in dispersal and survival. However, this influence is strongly dependent on the disturbance rate, pointing to an important interaction between spatial structure and disturbance. This interaction also plays a role in species coexistence. While spatial structure tends to reduce diversity in the absence of disturbance, the tendency is reversed when disturbance occurs. In conclusion, the spatial structure of communities is an important determinant of their diversity and characteristic traits. These traits are likely to influence important ecological properties such as resistance to invasion or response to climate change, which in turn will determine the fate of ecosystems facing the current global ecological crisis.

Diaspore traits discriminate good from weak colonisers on high elevation summits

The richness of plant species in Swiss alpine-nival summits increased during the climate warming of the 20th century. Thirty-seven summits (2797-3418 m a.s.l.) with both old (~1900-1920) and recent (~2000) plant inventories were used to test whether biological species traits can explain the observed rates of summit colonisation. Species were classified into two groups: good colonisers (colonising five or more summits) and weak colonisers (fewer than five new summits). We compared species traits related to growth, reproduction and dispersal between these two groups and between the good colonisers and a group of high alpine grassland species. The observed colonisation pattern was subsequently compared to a simulated random colonisation pattern. The distribution of new species on the summits was not random, and 16 species exhibited a colonisation rate higher than expected by chance. Taraxacum alpinum aggr. and Cardamine resedifolia were the best colonisers. Results showed that diaspore traits enhancing long-distance dispersal were more frequent among good colonisers than among weak colonisers. Good colonisers were mostly characterised by pappi or narrow wings on their diaspores. Both groups were able to grow on soils more bare and rocky than species from the alpine grasslands. All other biological traits that we considered were similar among the three alpine species groups. These results are important for improving predictive models of species distribution under climate change

Biotic and abiotic variables show little redundancy in explaining tree species distributions

Abiotic factors such as climate and soil determine the species fundamental niche, which is further constrained by biotic interactions such as interspecific competition. To parameterize this realized niche, species distribution models (SDMs) most often relate species occurrence data to abiotic variables, but few SDM studies include biotic predictors to help explain species distributions. Therefore, most predictions of species distributions under future climates assume implicitly that biotic interactions remain constant or exert only minor influence on large-scale spatial distributions, which is also largely expected for species with high competitive ability. We examined the extent to which variance explained by SDMs can be attributed to abiotic or biotic predictors and how this depends on species traits. We fit generalized linear models for 11 common tree species in Switzerland using three different sets of predictor variables: biotic, abiotic, and the combination of both sets. We used variance partitioning to estimate the proportion of the variance explained by biotic and abiotic predictors, jointly and independently. Inclusion of biotic predictors improved the SDMs substantially. The joint contribution of biotic and abiotic predictors to explained deviance was relatively small (similar to 9%) compared to the contribution of each predictor set individually (similar to 20% each), indicating that the additional information on the realized niche brought by adding other species as predictors was largely independent of the abiotic (topo-climatic) predictors. The influence of biotic predictors was relatively high for species preferably growing under low disturbance and low abiotic stress, species with long seed dispersal distances, species with high shade tolerance as juveniles and adults, and species that occur frequently and are dominant across the landscape. The influence of biotic variables on SDM performance indicates that community composition and other local biotic factors or abiotic processes not included in the abiotic predictors strongly influence prediction of species distributions. Improved prediction of species' potential distributions in future climates and communities may assist strategies for sustainable forest management.

Coexistence of specialist and generalist species is shaped by dispersal and environmental factors.

Disentangling the mechanisms mediating the coexistence of habitat specialists and generalists has been a long-standing subject of investigation. However, the roles of species traits and environmental and spatial factors have not been assessed in a unifying theoretical framework. Theory suggests that specialist species are more competitive in natural communities. However, empirical work has shown that specialist species are declining worldwide due to habitat loss and fragmentation. We addressed the question of the coexistence of specialist and generalist species with a spatially explicit metacommunity model in continuous and heterogeneous environments. We characterized how species' dispersal abilities, the number of interacting species, environmental spatial autocorrelation, and disturbance impact community composition. Our results demonstrated that species' dispersal ability and the number of interacting species had a drastic influence on the composition of metacommunities. More specialized species coexisted when species had large dispersal abilities and when the number of interacting species was high. Disturbance selected against highly specialized species, whereas environmental spatial autocorrelation had a marginal impact. Interestingly, species richness and niche breadth were mainly positively correlated at the community scale but were negatively correlated at the metacommunity scale. Numerous diversely specialized species can thus coexist, but both species' intrinsic traits and environmental factors interact to shape the specialization signatures of communities at both the local and global scales.

Biodiversity: predictive traits to the rescue

Climate change poses new challenges to the conservation of species, which at present requires data-hungry models to meaningfully anticipate future threats. Now a study suggests that species traits may offer a simpler way to help predict future extinction risks.

What matters for predicting spatial distributions of trees: techniques, data, or species' characteristics?

Data characteristics and species traits are expected to influence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presence-only data to model current distribution, which we evaluated against independent presence-absence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difficult to predict. The predictive power of species-distribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.

Species divergence and trait convergence in experimental plant community assembly

Despite decades of research, it remains controversial whether ecological communities converge towards a common structure determined by environmental conditions irrespective of assembly history. Here, we show experimentally that the answer depends on the level of community organization considered. In a 9-year grassland experiment, we manipulated initial plant composition on abandoned arable land and subsequently allowed natural colonization. Initial compositional variation caused plant communities to remain divergent in species identities, even though these same communities converged strongly in species traits. This contrast between species divergence and trait convergence could not be explained by dispersal limitation or community neutrality alone. Our results show that the simultaneous operation of trait-based assembly rules and species-level priority effects drives community assembly, making it both deterministic and historically contingent, but at different levels of community organization.

Which intrinsic traits predict vulnerability to extinction depends on the actual threatening processes

Understanding what makes some species more vulnerable to extinction than others is an important challenge for conservation. Many comparative analyses have addressed this issue exploring how intrinsic and extrinsic traits associate with general estimates of vulnerability. However, these general estimates do not consider the actual threats that drive species to extinction and hence, are more difficult to translate into effective management. We provide an updated description of the types and spatial distribution of threats that affect mammals globally using data from the IUCN for 5941 species of mammals. Using these data we explore the links between intrinsic species traits and specific threats in order to identify key intrinsic features associated with particular drivers of extinction. We find that families formed by small-size habitat specialists are more likely to be threatened by habitat-modifying processes; whereas, families formed by larger mammals with small litter sizes are more likely to be threatened by processes that directly affect survival. These results highlight the importance of considering the actual threatening process in comparative studies. We also discuss the need to standardize and rank threat importance in global assessments such as the IUCN Red List to improve our ability to understand what makes some species more vulnerable to extinction than others.

Ecological traits affect the sensitivity of bees to land-use pressures in European agricultural landscapes

1. Bees are a functionally important and economically valuable group, but are threatened byland-use conversion and intensification. Such pressures are not expected to affect all species identically; rather, they are likely to be mediated by the species’ ecological traits. 2. Understanding which types of species are most vulnerable under which land uses is an important step towards effective conservation planning.3. We collated occurrence and abundance data for 257 bee species at 1584 European sites from surveys reported in 30 published papers (70 056 records) and combined them with species-level ecological trait data. We used mixed-effects models to assess the importance of land use (land-use class, agricultural use-intensity and a remotely-sensed measure of vegetation),traits and trait 9 land-use interactions, in explaining species occurrence and abundance.4. Species’ sensitivity to land use was most strongly influenced by flight season duration and foraging range, but also by niche breadth, reproductive strategy and phenology, with effects that differed among cropland, pastoral and urban habitats.5. Synthesis and applications. Rather than targeting particular species or settings, conservation action s may be more effective if focused on mitigating situations where species’ traits strongly and negatively interact with land-use pressures. We find evidence that low-intensity agriculture can maintain relatively diverse bee communities; in more intensive settings, added floral resources may be beneficial, but will require careful placement with respect to foraging ranges of smaller bee species. Protection of semi-natural habitats is essential, however; in particular, conversion to urban environments could have severe effects on bee diversity and pollination services. Our results highlight the importance of exploring how ecological traits mediate species responses to human impacts, but further research is needed to enhance the predictive ability of such analyses.