TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

An operational definition of the biome for global change research

CITATION: Conradi, T. et al. 2020. An operational definition of the biome for global change research. New Phytologist, 227:1294–1306, doi:10.1111/nph.16580.The original publication is available at https://nph.onlinelibrary.wiley.comBiomes are constructs for organising knowledge on the structure and functioning of the world’s ecosystems, and serve as useful units for monitoring how the biosphere responds to anthropogenic drivers, including climate change. The current practice of delimiting biomes relies on expert knowledge. Recent studies have questioned the value of such biome maps for comparative ecology and global-change research, partly due to their subjective origin. Here we propose a flexible method for developing biome maps objectively. The method uses range modelling of several thousands of plant species to reveal spatial attractors for different growth-form assemblages that define biomes. The workflow is illustrated using distribution data from 23 500 African plant species. In an example application, we create a biome map for Africa and use the fitted species models to project biome shifts. In a second example, we map gradients of growth-form suitability that can be used to identify sites for comparative ecology. This method provides a flexible framework that (1) allows a range of biome types to be defined according to user needs and (2) enables projections of biome changes that emerge purely from the individualistic responses of plant species to environmental changes.Publisher's versio

Mismatches between demographic niches and geographic distributions are strongest in poorly dispersed and highly persistent plant species.

The ecological niche of a species describes the variation in population growth rates along environmental gradients that drives geographic range dynamics. Niches are thus central for understanding and forecasting species' geographic distributions. However, theory predicts that migration limitation, source-sink dynamics, and time-lagged local extinction can cause mismatches between niches and geographic distributions. It is still unclear how relevant these niche-distribution mismatches are for biodiversity dynamics and how they depend on species life-history traits. This is mainly due to a lack of the comprehensive, range-wide demographic data needed to directly infer ecological niches for multiple species. Here we quantify niches from extensive demographic measurements along environmental gradients across the geographic ranges of 26 plant species (Proteaceae; South Africa). We then test whether life history explains variation in species' niches and niche-distribution mismatches. Niches are generally wider for species with high seed dispersal or persistence abilities. Life-history traits also explain the considerable interspecific variation in niche-distribution mismatches: poorer dispersers are absent from larger parts of their potential geographic ranges, whereas species with higher persistence ability more frequently occupy environments outside their ecological niche. Our study thus identifies major demographic and functional determinants of species' niches and geographic distributions. It highlights that the inference of ecological niches from geographical distributions is most problematic for poorly dispersed and highly persistent species. We conclude that the direct quantification of ecological niches from demographic responses to environmental variation is a crucial step toward a better predictive understanding of biodiversity dynamics under environmental change

An operational definition of the biome for global change research

CITATION: Conradi, T. et al. 2020. An operational definition of the biome for global change research. New Phytologist, 227:1294–1306, doi:10.1111/nph.16580.The original publication is available at https://nph.onlinelibrary.wiley.comBiomes are constructs for organising knowledge on the structure and functioning of the world’s ecosystems, and serve as useful units for monitoring how the biosphere responds to anthropogenic drivers, including climate change. The current practice of delimiting biomes relies on expert knowledge. Recent studies have questioned the value of such biome maps for comparative ecology and global-change research, partly due to their subjective origin. Here we propose a flexible method for developing biome maps objectively. The method uses range modelling of several thousands of plant species to reveal spatial attractors for different growth-form assemblages that define biomes. The workflow is illustrated using distribution data from 23 500 African plant species. In an example application, we create a biome map for Africa and use the fitted species models to project biome shifts. In a second example, we map gradients of growth-form suitability that can be used to identify sites for comparative ecology. This method provides a flexible framework that (1) allows a range of biome types to be defined according to user needs and (2) enables projections of biome changes that emerge purely from the individualistic responses of plant species to environmental changes.Publisher's versio

Systematics and evolution of predatory flower flies (Diptera Syrphidae) based on exon-capture sequencing

Flower flies (Diptera: Syrphidae) are one of the most species-rich dipteran families and provide important ecosystem services such as pollination, biological control of pests, recycling of organic matter and redistributions of essential nutrients. Flower fly adults generally feed on pollen and nectar, but their larval feeding habits are strikingly diverse. In the present study, high-throughput sequencing was used to capture and enrich phylogenetically and evolutionary informative exonic regions. With the help of the baitfisher software, we developed a new bait kit (SYRPHIDAE1.0) to target 1945 CDS regions belonging to 1312 orthologous genes. This new bait kit was successfully used to exon capture the targeted loci in 121 flower fly species across the different subfamilies of Syrphidae. We analysed different amino acid and nucleotide data sets (1302 loci and 154 loci) with maximum likelihood and multispecies coalescent models. Our analyses yielded highly supported similar topologies, although the degree of the SRH (global stationarity, reversibility and homogeneity) conditions varied greatly between amino acid and nucleotide data sets. The sisterhood of subfamilies Pipizinae and Syrphinae is supported in all our analyses, confirming a common origin of taxa feeding on soft-bodied arthropods. Based on our results, we define Syrphini stat.rev. to include the genera Toxomerus and Paragus. Our divergence estimate analyses with beast inferred the origin of the Syrphidae in the Lower Cretaceous (125.5-98.5 Ma) and the diversification of predatory flower flies around the K-Pg boundary (70.61-54.4 Ma), coinciding with the rise and diversification of their prey.Peer reviewe

MscS-like mechanosensitive channels in plants and microbes

The challenge of osmotic stress is something all living organisms must face as a result of environmental dynamics. Over the past three decades, innovative research and cooperation across disciplines have irrefutably established that cells utilize mechanically gated ion channels to release osmolytes and prevent cell lysis during hypoosmotic stress. Early electrophysiological analysis of the inner membrane of Escherichia coli identified the presence of three distinct mechanosensitive activities. The subsequent discoveries of the genes responsible for two of these activities, the mechanosensitive channels of large (MscL) and small (MscS) conductance, led to the identification of two diverse families of mechanosensitive channels. The latter of these two families, the MscS family, consists of members from bacteria, archaea, fungi, and plants. Genetic and electrophysiological analysis of these family members has provided insight into how organisms use mechanosensitive channels for osmotic regulation in response to changing environmental and developmental circumstances. Furthermore, determining the crystal structure of E. coli MscS and several homologues in several conformational states has contributed to our understanding of the gating mechanisms of these channels. Here we summarize our current knowledge of MscS homologues from all three domains of life and address their structure, proposed physiological functions, electrophysiological behaviors, and topological diversity

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Peer reviewe

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives