The scope of language contact as a constraint factor in language change: The periphrasis haber de plus infinitive in a corpus of language immediacy in modern Spanish

In this work an empirical study grounded in the principles and methods of the comparative variationist framework is conducted to measure the scope of language contact as a factor constraining some potentially diverging uses of a Spanish verbal periphrasis that has undergone a sharp decline over the last century (haber de plus infinitive). The analysis is based on three independent samples of text that correspond to three dialectal areas of peninsular Spanish (monolingual zones, Catalan-speaking linguistic territories and the north-western linguistic area). These samples, extracted from a corpus made up of texts of communicative immediacy from the 19th and the first half of the 20th centuries, confirm the existence of a certain linguistic convergence in the expressive habits of the speakers in the bilingual communities. In each region, however, the outcomes are different, due to parallel differences in the structural position of the periphrasis in each language. However, a thorough analysis of the variable context that surrounds the periphrasis shows that the observed differences do not affect the essence of the underlying grammar of this variant, whose decline (which favours tener que plus infinitive and becomes faster as the 20th century advances) is constrained by identical linguistic and extralinguistic conditioning factors in all the dialectal areas

Low growth resilience to drought is related to future mortality risk in trees

Severe droughts have the potential to reduce forest productivity and trigger tree mortality. Most trees face several drought events during their life and therefore resilience to dry conditions may be crucial to long-term survival. We assessed how growth resilience to severe droughts, including its components resistance and recovery, is related to the ability to survive future droughts by using a tree-ring database of surviving and now-dead trees from 118 sites (22 species, >3,500 trees). We found that, across the variety of regions and species sampled, trees that died during water shortages were less resilient to previous non-lethal droughts, relative to coexisting surviving trees of the same species. In angiosperms, drought-related mortality risk is associated with lower resistance (low capacity to reduce impact of the initial drought), while it is related to reduced recovery (low capacity to attain pre-drought growth rates) in gymnosperms. The different resilience strategies in these two taxonomic groups open new avenues to improve our understanding and prediction of drought-induced mortality.Fil: DeSoto, Lucía. Consejo Superior de Investigaciones Científicas; España. Universidad de Coimbra; PortugalFil: Cailleret, Maxime. Eidgenössische Technische Hochschule Züric; Suiza. Université Aix-marseille; Francia. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Sterck, Frank. University of Agriculture Wageningen; Países BajosFil: Jansen, Steven. Universitat Ulm; AlemaniaFil: Kramer, Koen. University of Agriculture Wageningen; Países Bajos. Land Life Company; Países BajosFil: Robert, Elisabeth M. R.. Creaf; España. Vrije Unviversiteit Brussel; Bélgica. Royal Museum for Central Africa; BélgicaFil: Aakala, Tuomas. University of Helsinki; FinlandiaFil: Amoroso, Mariano Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. - Universidad Nacional de Rio Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; ArgentinaFil: Bigler, Christof. Eidgenössische Technische Hochschule Züric; SuizaFil: Camarero, J. Julio. Consejo Superior de Investigaciones Científicas; EspañaFil: Čufar, Katarina. University 0f Ljubljana; EsloveniaFil: Gea Izquierdo, Guillermo. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria; EspañaFil: Gillner, Sten. Technische Universität Dresden; AlemaniaFil: Haavik, Laurel J.. Servicio Forestal de los Estados Unidos; Estados UnidosFil: Hereş, Ana Maria. Basque Centre For Climate Change; España. Transilvania University of Brasov; RumaniaFil: Kane, Jeffrey M.. Humboldt State University; Estados UnidosFil: Kharuk, Vyacheslav I.. Siberian Federal University; Rusia. Siberian Division of the Russian Academy of Sciences; RusiaFil: Kitzberger, Thomas. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Klein, Tamir. Weizmann Institute of Science; IsraelFil: Levanič, Tom. Slovenian Forestry Institute; EsloveniaFil: Linares, Juan C.. Universidad Pablo de Olavide; EspañaFil: Mäkinen, Harri. Natural Resources Institute Finland; FinlandiaFil: Oberhuber, Walter. Universidad de Innsbruck; AustriaFil: Papadopoulos, Andreas. Geoponiko Panepistimion Athinon; GreciaFil: Rohner, Brigitte. Eidgenössische Technische Hochschule Zürich; Suiza. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Sangüesa Barreda, Gabriel. Universidad de Valladolid; EspañaFil: Stojanovic, Dejan B.. University of Novi Sad; SerbiaFil: Suarez, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; ArgentinaFil: Villalba, Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Martínez Vilalta, Jordi. Universitat Autònoma de Barcelona; España. Creaf; Españ

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

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

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