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Dryad Translation missing: fr.blacklight.search.logo
Aubin, Isabelle; Cardou, Françoise; Munson, Alison; Anand, Madhur; Arsenault, André; Bell, F. Wayne; Bergeron, Yves; Boulangeat, Isabelle; Delagrange, Sylvain; Fenton, Nicole J.; Gravel, Dominique; Hébert, François; Johnstone, Jill; Macdonald, S. Ellen; Mallik, Azim; McIntosh, Anne C.S.; McLaren, Jennie R.; Messier, Christian; Morris, Dave; Shipley, Bill; Sirois, Luc; Thiffault, Nelson; Boisvert-Marsh, Laura; Kumordzi, Bright B. 2022-04-18 <p class="MsoPlainText">Intraspecific trait variability (ITV) provides the material for species adaptation to environmental changes. To advance our understanding of how ITV can contribute to species adaptation to a wide range of environmental conditions, we studied five widespread understory forest species exposed to both continental-scale climate gradients, and local soil and disturbance gradients. We investigated the environmental drivers of between-site leaf and root trait variation, and tested whether higher between-site ITV was associated with increased trait sensitivity to environmental variation (i.e. environmental fit).</p> <p class="MsoPlainText">We measured morphological (specific leaf area: SLA, specific root length: SRL) and chemical traits (Leaf and Root N, P, K, Mg, Ca) of five forest understory vascular plant<span style="font-family:KievitWeb , sans-serif;font-size:medium;"> </span>species at 78 sites across Canada. A total of 261 species-by-site combinations spanning ~4300 km were sampled, capturing important abiotic and biotic environmental gradients (neighbourhood composition, canopy structure, soil conditions, climate). We used multivariate and univariate linear mixed models to identify drivers of ITV and test the association of between-site ITV with environmental fit.</p> <p class="MsoPlainText">Between-site ITV of leaf traits was primarily driven by canopy structure and climate. Comparatively, environmental drivers explained only a small proportion of variability in root traits: these relationships were trait-specific and included soil conditions (Root P), canopy structure (Root N) and neighbourhood composition (SRL, Root K). Between-site ITV was associated with increased environmental fit only for a minority of traits, primarily in response to climate (SLA, Leaf N, SRL).</p> <p class="MsoPlainText">Synthesis. By studying how ITV is structured along environmental gradients among species adapted to a wide range of conditions, we can begin to understand how individual species might respond to environmental change. Our results show that generalizable trait-environment relationships occur primarily aboveground and only accounted for a small proportion of variability. For our group of species with broad ecological niches, variability in traits was only rarely associated with higher environmental fit, and primarily along climatic gradients. These results point to promising research avenues on the various ways in which trait variation can affect species performance along different environmental gradients.</p> https://creativecommons.org/publicdomain/zero/1.0/
Zenodo Translation missing: fr.blacklight.search.logo
Zenodo
Hsu, Alice; Jones, Matthew; Smith, Adam J. P.; Thurgood, Jane; Abatzoglou, John; Kolden, Crystal; Anderson, Liana O.; Clarke, Hamish; Doerr, Stefan; Fernandes, Paulo M.; Santín, Cristina; Strydom, Tercia; Le Quéré, Corinne; Ascoli, Davide; Baard, Johan; Bergius, Niclas; Carlsson, Julia; Castellnou, Marc; Cheney, Chad; Elliot, Andy; Evans, Jay; Guiomar, Nuno; Hiers, John; Kukavskaya, Elena A.; Prat-Guitart, Nuria; Rigolot, Eric; Roman-Cuesta, Rosa Maria; Tanpipat, Veerachai; Varner, Morgan; Yamashita, Youhei; Lopez Valverde, Jose Alejandro; Barreto, Ricardo; Becerra, Javier; Druce, David; Falleiro, Rodrigo; Macher, Lisa; Morris, Dave; Park, Jane; Robles, César; Rücker, Gernot; Senra, Francisco; Zerr, Emma 2024-09-19 File Name File Type Description ERA5_CEMS_Download_and_Resample_Notebooks.zip ZIP file containing Python Jupyter notebooks Code used to download and resample ERA5 and CEMS meteorological data from hourly into daily values Geolocate_GlobalRx_Notebooks.zip ZIP file containing Python Jupyter notebooks Code used to determine values of meteorological and environmental variables at date and location of each burn record GlobalRx-Figures-Stats.ipynb Jupyter notebook Code used to calculate and generate all statistics and figures in the paper GlobalRx_CSV_v2024.1.csv GlobalRx_XLSX_v2024.1.xlsx GlobalRx_SHP_v2024.1.zip CSV, Excel, and ZIP file containing shape file and accompanying feature files GlobalRx dataset. Features of the dataset are described in more detail below.**   **Description of GlobalRx Dataset: 198,890 records of prescribed burns in 16 countries. In the information below, the name of the variable's column within the dataset is given in parentheses () in code font. For example, the column with the Drought Code data is titled DC. For each record, the following general information (derived from the original burn records sources) is included, where available: Latitude (Latitude) Longitude (Longitude) Year (Year) Month (Month)  Day (Day) Time* (Time) DOY (DOY) Country (Country) State/Province (State/Province) Agency/Organisation (Agency/Organisation) Burn Objective* (Burn Objective) Area Burned (Ha)* (Area Burned (Ha)) Data Repository (Data Repository) Citation (Citation) * Not available for every record For each record, the following meteorological information (derived from the ERA5 single levels reanalysis product) is also included: Daily total accumulated precipitation (PPT_tot) Daily minimum and mean relative humidity (RH_min, RH_mean)* Daily maximum 2-meter temperature (T_max) Daily maximum and mean 10-meter wind speed (Wind_max, Wind_mean) Daily minimum boundary layer height (BLH_min) C-Haines Index (CHI)* Vapor pressure deficit (VPD)* * Computed from other ERA5 meteorological variables. For each record, the following fire weather indices and components (derived from ERA5 fire weather reanalysis product) are also included: Canadian fire weather index (FWI) Fine fuel moisture code (FFMC) Drought moisture code (DMC) Drought code (DC) McArthur forest fire danger index (FFDI) Keetch-Byram drought index (KBDI) US burning index (USBI) For each record, the following environmental information (derived from various sources, see paper for more information) is also included: Ecoregion (Olson et al. 2001) (Ecoregion (Olson)) Biome (Olson et al. 2001) (Biome (Olson)) Koppen Climate (Beck et al. 2023) (Koppen Climate) Topography (Danielson and Gesch 2011) (Topography) Fuelbed Classification (GFD-FCCS) (Pettinari and Chuvieco 2016) (Fuelbed Classification (GFD-FCCS)) WDPA Name (WDPA 2024) (WDPA Name) WDPA Governance (WDPA 2024) (WDPA Governance) WDPA Ownership (WDPA 2024) (WDPA Ownership) WDPA Designation (WDPA 2024) (WDPA Designation) WDPA IUCN Category (WDPA 2024) (WDPA IUCN Category) https://creativecommons.org/licenses/by/4.0/legalcode

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