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Research data keyboard_double_arrow_right Dataset 2024Embargo end date: 12 Mar 2024Publisher:Dryad # Land use change converts temperate dryland landscape into a net methane source Raw flux data for methane, carbon dioxide, and other species were measured using a paired Picarro-Licor trace gas analyzer from June – August 2021 (flux data is in `ghg_raw.csv`, data for statistical analysis in `ghg_stats.csv`). Net nitrogen mineralization data was collected through ion exchange resins (data is in `n_raw.csv`). Bulk density, soil texture, pH, and soil carbon and nitrogen were completed on soil samples analyzed at Yale University (data is in `soilcn.csv`). Carbon and nitrogen were measured on an elemental analyzer. Soil texture was estimated using particle size analysis. pH was measured using a bench top pH meter. Land cover classification was done using the NatureServe database for Sublette County, WY (data is in `landcover.xlsx` and in landcover.zip). In 'included' tab on landcover.xlsx, there are two sections of data on the left and right, not one rectangular table of data. ## Description of the data and file structure #### ghg\_raw\.csv: * date: date of sampling (mm/dd/yy) * week: week of sampling (Week 1 - Week 11) * id: soil collar ID * ranch: site name (site1-3) * location: one of the four land cover types measured [meadow (hay meadow); bog (introduced wetland); slsage (sloping sagebrush); upsage (upland sagebrush)] * sh/is: indicates whether sample was taken under shrub or in the interspace * stemp: soil temperature ('b0C) * pcent_sand: % sand content * ph: soil pH * chamber offset: height of soil collar from the surface of soil (measured in cm) * volume add: volume added through extension of chamber attachment (cm3) * start time: start time of sampling (hour:minute:second) * end time: end time of sampling (hour:minute:second) * time off: offset from the time read by the two instruments (s) * co2 flux: carbon dioxide flux measured over 8 minute period (umol co2 m-2 s-1) * co2 rsq: R-squared of exponential fit of values over 8 minute period * ch4 flux: methane flux measured over 8 minute period (umol ch4 m-2 s-1) * ch4 rsq: R-squared of exponential fit of values over 8 minute period * n2o flux: nitrous oxide flux measured over 8 minute period (umol n2o m-2 s-1) * n2o rsq: R-squared of exponential fit of values over 8 minute period * nh3 flux: ammonia flux measured over 8 minute period (umol nh3 m-2 s-1) * nh3 rsq: R-squared of exponential fit of values over 8 minute period * h2o flux: h2o flux (% water vapor) * h2o rsq: R-squared of exponential fit of values over 8 minute period * notes: anything important to note from data collection of that day #### ghgstats.csv: * date: date of sampling (mm/dd/yy) * week: week of sampling (Week 1 - Week 11) * id: soil collar ID * ranch: site name (site1-3) * location: one of the four land cover types measured [meadow (hay meadow); bog (introduced wetland); slsage (sloping sagebrush); upsage (upland sagebrush)] * stemp: soil temperature ('b0C) * h2o flux: h2o flux (% water vapor) * h2o rsq: R-squared of exponential fit of values over 8 minute period * sand: % sand content (0-5 cm) * clay: % clay content (0-5 cm) * pH: soil pH (0-5 cm) * carbon: % soil carbon (0-5 cm) * nitrogen: % soil nitrogen (0-5 cm) * co2 flux: carbon dioxide flux measured over 8 minute period (umol co2 m-2 s-1) * co2 rsq: R-squared of exponential fit of values over 8 minute period * ch4 flux: methane flux measured over 8 minute period (umol ch4 m-2 s-1) * ch4 rsq: R-squared of exponential fit of values over 8 minute period #### n\_raw\.csv: * ranch: site name (site1-3) * location: one of the four land cover types measured [meadow (hay meadow); bog (introduced wetland); slsage (sloping sagebrush); upsage (upland sagebrush)] * no3: nitrate concentrations (ug no3/10cm2/burial length) * nh4: ammonium concentrations (ug nh4/10cm2/burial length) * fe: iron concentrations (ug fe/10cm2/burial length) * s: sulfur concentrations (ug s/10cm2/burial length) #### soilcn.csv: * ranch: site name (site1-3) * location: one of the four land cover types measured [meadow (hay meadow); bog (introduced wetland); slsage (sloping sagebrush); upsage (upland sagebrush)] * depth: depth of soil sample (cm) * finebulkd: fine soil bulk density (g soil cm-3) * c: % soil carbon (0-5 cm) * n: % soil N (0-5 cm) * cpool: total soil carbon for 0-5 cm (g C m-2) * npool: total soil N for 0-5 cm (g N m-2) #### landcover.xlsx: Sheet1 (ecosystems): * gridcode: NatureServe grid code assignment * Shape Area: area covered by a given land cover type (m2) * Value: grid code assignment * Count: Number of land cover types classified under a given name within the county boundary * ECOLSYS_LU: Land cover type classification under NatureServe categories Sheet2 (included): * ecotype: Land cover type classification under NatureServe categories * shape area: area covered by a given land cover type (m2) * Total shape area: area covered by landscape of included cover types (m2) * % introduced wetland: % of area represented by introduced wetland cover types within landscape * % hay meadow: % of area represented by hay meadow cover types within landscape * % big sagebrush: % of area represented by big sagebrush cover types within landscape #### **landcover.zip** * landcover.dbf: database file, attribute data * landcover.prj: projection file, coordinate system * landcover.shp: shapefile, stores geometry of spatial data * landcover.shx: shape index file ## Code/Software #### figs.R: used to make the primary figures for the manuscript and supplementary figures Completed in R (version 4.4.2) Packages: `ggplot2`, `wesanderson`, `stringr`, `zoo`, `tidyverse`, `scales`, `ggpubr`, `cowplot`, `gt`, `dplyr`, 'sf' #### stats.R: -used to do the statistical analysis for the manuscript -Completed in R (version 4.4.2) -Packages: 'pracma', `tidyverse`, `ggpubr`, `rstatix`, `lme4`, 'lmerTest', `emmeans`, 'car', `MASS`, `glmm` ## **Supplementary ** scalefigure.pptx * used to make figure 6 for manuscript (weighted cumulative methane flux by cover type) ## Changes from previous version: * Created a few different figures (one land cover map and made some edits to existing figures). The script 'figs.r' reflects those changes. I added the land cover shapefile I used to make the land cover map (landcover.zip). I ran a few new analyses on cumulative gas fluxes, which is included in the script, 'stats.r'. All other files are the same. Drylands cover approximately 40% of the global land surface and are thought to contribute significantly to the soil methane sink. However, large-scale methane budgets have not fully considered the influence of agricultural land use change in drylands, which often includes irrigation to create land cover types that support hay or grains for livestock production. These land cover types may represent a small proportion of the landscape but could disproportionately contribute to greenhouse gas exchange and are currently omitted in estimates of dryland methane fluxes. We measured greenhouse gas fluxes among big sagebrush, introduced wetlands, and hay meadows in a semi-arid temperate dryland in Wyoming, USA to investigate how these small-scale irrigated land cover types contributed to landscape-scale methane dynamics. Big sagebrush ecosystems dominated the landscape while the introduced wetlands and hay meadows represented 1% and 12%, respectively. Methane uptake was consistent in the big sagebrush ecosystems, emissions and uptake were variable in the hay meadows, and emissions were consistent in the introduced wetlands. Despite making up 1% of the total land area, methane production in the introduced wetlands overwhelmed consumption occurring throughout the rest of the landscape, making this region a net methane source. Our work suggests that introduced wetlands and other irrigated land cover types created for livestock production may represent a significant, previously overlooked source of anthropogenic methane in this region and perhaps in drylands globally. Raw flux data for methane, carbon dioxide, and other species were measured using a paired Picarro-Licor trace gas analyzer from June – August 2021 (flux data is in ghg_raw.csv, data for statistical analysis in ghg_stats.csv). Net nitrogen mineralization data was collected through ion exchange resins (data is in n_raw.csv). Bulk density, soil texture, pH, and soil carbon and nitrogen were completed on soil samples analyzed at Yale University (data is in soilcn.csv). Carbon and nitrogen were measured on an elemental analyzer. Soil texture was estimated using particle size analysis. pH was measured using a bench top pH meter. Land cover classification was done using the NatureServe database for Sublette County, WY (data used for scaling is in landcover.xlsx and shapefile is in landcover.zip). In 'included' tab on landcover.xlsx, there are two sections of data on the left and right, not one rectangular table of data.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Embargo end date: 12 Mar 2024Publisher:Dryad # Land use change converts temperate dryland landscape into a net methane source Raw flux data for methane, carbon dioxide, and other species were measured using a paired Picarro-Licor trace gas analyzer from June – August 2021 (flux data is in `ghg_raw.csv`, data for statistical analysis in `ghg_stats.csv`). Net nitrogen mineralization data was collected through ion exchange resins (data is in `n_raw.csv`). Bulk density, soil texture, pH, and soil carbon and nitrogen were completed on soil samples analyzed at Yale University (data is in `soilcn.csv`). Carbon and nitrogen were measured on an elemental analyzer. Soil texture was estimated using particle size analysis. pH was measured using a bench top pH meter. Land cover classification was done using the NatureServe database for Sublette County, WY (data is in `landcover.xlsx` and in landcover.zip). In 'included' tab on landcover.xlsx, there are two sections of data on the left and right, not one rectangular table of data. ## Description of the data and file structure #### ghg\_raw\.csv: * date: date of sampling (mm/dd/yy) * week: week of sampling (Week 1 - Week 11) * id: soil collar ID * ranch: site name (site1-3) * location: one of the four land cover types measured [meadow (hay meadow); bog (introduced wetland); slsage (sloping sagebrush); upsage (upland sagebrush)] * sh/is: indicates whether sample was taken under shrub or in the interspace * stemp: soil temperature ('b0C) * pcent_sand: % sand content * ph: soil pH * chamber offset: height of soil collar from the surface of soil (measured in cm) * volume add: volume added through extension of chamber attachment (cm3) * start time: start time of sampling (hour:minute:second) * end time: end time of sampling (hour:minute:second) * time off: offset from the time read by the two instruments (s) * co2 flux: carbon dioxide flux measured over 8 minute period (umol co2 m-2 s-1) * co2 rsq: R-squared of exponential fit of values over 8 minute period * ch4 flux: methane flux measured over 8 minute period (umol ch4 m-2 s-1) * ch4 rsq: R-squared of exponential fit of values over 8 minute period * n2o flux: nitrous oxide flux measured over 8 minute period (umol n2o m-2 s-1) * n2o rsq: R-squared of exponential fit of values over 8 minute period * nh3 flux: ammonia flux measured over 8 minute period (umol nh3 m-2 s-1) * nh3 rsq: R-squared of exponential fit of values over 8 minute period * h2o flux: h2o flux (% water vapor) * h2o rsq: R-squared of exponential fit of values over 8 minute period * notes: anything important to note from data collection of that day #### ghgstats.csv: * date: date of sampling (mm/dd/yy) * week: week of sampling (Week 1 - Week 11) * id: soil collar ID * ranch: site name (site1-3) * location: one of the four land cover types measured [meadow (hay meadow); bog (introduced wetland); slsage (sloping sagebrush); upsage (upland sagebrush)] * stemp: soil temperature ('b0C) * h2o flux: h2o flux (% water vapor) * h2o rsq: R-squared of exponential fit of values over 8 minute period * sand: % sand content (0-5 cm) * clay: % clay content (0-5 cm) * pH: soil pH (0-5 cm) * carbon: % soil carbon (0-5 cm) * nitrogen: % soil nitrogen (0-5 cm) * co2 flux: carbon dioxide flux measured over 8 minute period (umol co2 m-2 s-1) * co2 rsq: R-squared of exponential fit of values over 8 minute period * ch4 flux: methane flux measured over 8 minute period (umol ch4 m-2 s-1) * ch4 rsq: R-squared of exponential fit of values over 8 minute period #### n\_raw\.csv: * ranch: site name (site1-3) * location: one of the four land cover types measured [meadow (hay meadow); bog (introduced wetland); slsage (sloping sagebrush); upsage (upland sagebrush)] * no3: nitrate concentrations (ug no3/10cm2/burial length) * nh4: ammonium concentrations (ug nh4/10cm2/burial length) * fe: iron concentrations (ug fe/10cm2/burial length) * s: sulfur concentrations (ug s/10cm2/burial length) #### soilcn.csv: * ranch: site name (site1-3) * location: one of the four land cover types measured [meadow (hay meadow); bog (introduced wetland); slsage (sloping sagebrush); upsage (upland sagebrush)] * depth: depth of soil sample (cm) * finebulkd: fine soil bulk density (g soil cm-3) * c: % soil carbon (0-5 cm) * n: % soil N (0-5 cm) * cpool: total soil carbon for 0-5 cm (g C m-2) * npool: total soil N for 0-5 cm (g N m-2) #### landcover.xlsx: Sheet1 (ecosystems): * gridcode: NatureServe grid code assignment * Shape Area: area covered by a given land cover type (m2) * Value: grid code assignment * Count: Number of land cover types classified under a given name within the county boundary * ECOLSYS_LU: Land cover type classification under NatureServe categories Sheet2 (included): * ecotype: Land cover type classification under NatureServe categories * shape area: area covered by a given land cover type (m2) * Total shape area: area covered by landscape of included cover types (m2) * % introduced wetland: % of area represented by introduced wetland cover types within landscape * % hay meadow: % of area represented by hay meadow cover types within landscape * % big sagebrush: % of area represented by big sagebrush cover types within landscape #### **landcover.zip** * landcover.dbf: database file, attribute data * landcover.prj: projection file, coordinate system * landcover.shp: shapefile, stores geometry of spatial data * landcover.shx: shape index file ## Code/Software #### figs.R: used to make the primary figures for the manuscript and supplementary figures Completed in R (version 4.4.2) Packages: `ggplot2`, `wesanderson`, `stringr`, `zoo`, `tidyverse`, `scales`, `ggpubr`, `cowplot`, `gt`, `dplyr`, 'sf' #### stats.R: -used to do the statistical analysis for the manuscript -Completed in R (version 4.4.2) -Packages: 'pracma', `tidyverse`, `ggpubr`, `rstatix`, `lme4`, 'lmerTest', `emmeans`, 'car', `MASS`, `glmm` ## **Supplementary ** scalefigure.pptx * used to make figure 6 for manuscript (weighted cumulative methane flux by cover type) ## Changes from previous version: * Created a few different figures (one land cover map and made some edits to existing figures). The script 'figs.r' reflects those changes. I added the land cover shapefile I used to make the land cover map (landcover.zip). I ran a few new analyses on cumulative gas fluxes, which is included in the script, 'stats.r'. All other files are the same. Drylands cover approximately 40% of the global land surface and are thought to contribute significantly to the soil methane sink. However, large-scale methane budgets have not fully considered the influence of agricultural land use change in drylands, which often includes irrigation to create land cover types that support hay or grains for livestock production. These land cover types may represent a small proportion of the landscape but could disproportionately contribute to greenhouse gas exchange and are currently omitted in estimates of dryland methane fluxes. We measured greenhouse gas fluxes among big sagebrush, introduced wetlands, and hay meadows in a semi-arid temperate dryland in Wyoming, USA to investigate how these small-scale irrigated land cover types contributed to landscape-scale methane dynamics. Big sagebrush ecosystems dominated the landscape while the introduced wetlands and hay meadows represented 1% and 12%, respectively. Methane uptake was consistent in the big sagebrush ecosystems, emissions and uptake were variable in the hay meadows, and emissions were consistent in the introduced wetlands. Despite making up 1% of the total land area, methane production in the introduced wetlands overwhelmed consumption occurring throughout the rest of the landscape, making this region a net methane source. Our work suggests that introduced wetlands and other irrigated land cover types created for livestock production may represent a significant, previously overlooked source of anthropogenic methane in this region and perhaps in drylands globally. Raw flux data for methane, carbon dioxide, and other species were measured using a paired Picarro-Licor trace gas analyzer from June – August 2021 (flux data is in ghg_raw.csv, data for statistical analysis in ghg_stats.csv). Net nitrogen mineralization data was collected through ion exchange resins (data is in n_raw.csv). Bulk density, soil texture, pH, and soil carbon and nitrogen were completed on soil samples analyzed at Yale University (data is in soilcn.csv). Carbon and nitrogen were measured on an elemental analyzer. Soil texture was estimated using particle size analysis. pH was measured using a bench top pH meter. Land cover classification was done using the NatureServe database for Sublette County, WY (data used for scaling is in landcover.xlsx and shapefile is in landcover.zip). In 'included' tab on landcover.xlsx, there are two sections of data on the left and right, not one rectangular table of data.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Embargo end date: 28 Sep 2021Publisher:Dryad Authors:Roberts, Kevin;
Roberts, Kevin
Roberts, Kevin in OpenAIRERank, Nathan;
Dahlhoff, Elizabeth; Stillman, Jonathon; +1 AuthorsRank, Nathan
Rank, Nathan in OpenAIRERoberts, Kevin;
Roberts, Kevin
Roberts, Kevin in OpenAIRERank, Nathan;
Dahlhoff, Elizabeth; Stillman, Jonathon; Williams, Caroline;Rank, Nathan
Rank, Nathan in OpenAIREdoi: 10.6078/d1rd88
Snow insulates the soil from air temperature, decreasing winter cold stress and altering energy use for organisms that overwinter in the soil. As climate change alters snowpack and air temperatures, it is critical to account for the role of snow in modulating vulnerability to winter climate change. Along elevational gradients in snowy mountains, snow cover increases but air temperature decreases, and it is unknown how these opposing gradients impact performance and fitness of organisms overwintering in the soil. We developed experimentally validated ecophysiological models of cold and energy stress over the past decade for the montane leaf beetle Chrysomela aeneicollis, along five replicated elevational transects in the Sierra Nevada mountains in California. Cold stress peaks at mid-elevations, while high elevations are buffered by persistent snow cover, even in dry years. While protective against cold, snow increases energy stress for overwintering beetles, particularly at low elevations, potentially leading to mortality or energetic trade-offs. Declining snowpack will predominantly impact mid-elevation populations by increasing cold exposure, while high elevation habitats may provide refugia as drier winters become more common. Climate Data This is the full data set that includes all temperature measures used in cold exposure pricipal component analysis and energy use model output for each winter at each site included in study. The second tab includes metadata for each column. Physiology Data This is the data for all beetles used in the field snow manipulation experiment and all biochemichal assays performed. It includes total protein, glycerol, sorbitol, glucose, and triacylglycerides. Respirometry This is the data set containing all respirometry data used in making the energy use model.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.6078/d1rd88&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 8visibility views 8 download downloads 2 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.6078/d1rd88&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Embargo end date: 28 Sep 2021Publisher:Dryad Authors:Roberts, Kevin;
Roberts, Kevin
Roberts, Kevin in OpenAIRERank, Nathan;
Dahlhoff, Elizabeth; Stillman, Jonathon; +1 AuthorsRank, Nathan
Rank, Nathan in OpenAIRERoberts, Kevin;
Roberts, Kevin
Roberts, Kevin in OpenAIRERank, Nathan;
Dahlhoff, Elizabeth; Stillman, Jonathon; Williams, Caroline;Rank, Nathan
Rank, Nathan in OpenAIREdoi: 10.6078/d1rd88
Snow insulates the soil from air temperature, decreasing winter cold stress and altering energy use for organisms that overwinter in the soil. As climate change alters snowpack and air temperatures, it is critical to account for the role of snow in modulating vulnerability to winter climate change. Along elevational gradients in snowy mountains, snow cover increases but air temperature decreases, and it is unknown how these opposing gradients impact performance and fitness of organisms overwintering in the soil. We developed experimentally validated ecophysiological models of cold and energy stress over the past decade for the montane leaf beetle Chrysomela aeneicollis, along five replicated elevational transects in the Sierra Nevada mountains in California. Cold stress peaks at mid-elevations, while high elevations are buffered by persistent snow cover, even in dry years. While protective against cold, snow increases energy stress for overwintering beetles, particularly at low elevations, potentially leading to mortality or energetic trade-offs. Declining snowpack will predominantly impact mid-elevation populations by increasing cold exposure, while high elevation habitats may provide refugia as drier winters become more common. Climate Data This is the full data set that includes all temperature measures used in cold exposure pricipal component analysis and energy use model output for each winter at each site included in study. The second tab includes metadata for each column. Physiology Data This is the data for all beetles used in the field snow manipulation experiment and all biochemichal assays performed. It includes total protein, glycerol, sorbitol, glucose, and triacylglycerides. Respirometry This is the data set containing all respirometry data used in making the energy use model.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 8visibility views 8 download downloads 2 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Embargo end date: 06 Mar 2024Publisher:Dryad Field leaf trait and spectroscopy data – We used leaf trait and spectral data from an extensive field campaign along an elevation gradient (from 3500 m to 220 m elevation) in the Peruvian Amazon where leaf traits for 60-80% of basal area of trees >10cm DBH were measured within a well-studied 1 ha plot network from April – November 2013 (Enquist et al., 2017). In each one ha plot (N=10 plots), we sampled the most abundant species as determined through basal area weighting (enough species generally to cover ~80% of the plot’s basal area). For each species, we sampled the five (three in the lowlands) largest trees (based on diameter at breast height (DBH)) and sampled one sun and one shade branch. On each of these branches, leaf chemistry and leaf mass area (LMA) were measured with the methodology detailed in Asner et al. (2014). On five randomly selected leaves for each branch, we measured hemispherical reflectance with an ASD Fieldspec Handheld 2 with fiber optic cable, a contact probe that has its own calibrated light source, and a leaf clip (Analytical Spectral Devices High-Intensity Contact Probe and Leaf Clip, Boulder, Colorado, USA) following (Doughty et al., 2017). We measured leaf spectroscopy (400-1075 nm) on the same branches where the leaf traits were collected. Both LMA and Chlorophyll A had previously been shown with this dataset to have a correlation with leaf spectroscopy (Doughty et al., 2017). However, we had not previously tried to compare leaf spectral data with DBH directly. Plot data – Aboveground biomass - We used 2,102 of 19,160 total AGB field plots between +30° and -30° latitude classified as broadleaf evergreen trees by MODIS PFT using public data from Duncanson et al 2022 that was organized and publicly available through ORNL DAAC as an RDS (R data serialization) file. Distribution plots are shown in Fig S1 (AGB) and S2 (residuals). NPP and GPP - We also used 21, 1 ha plots where NPP and sometimes GPP were measured following the GEM protocol (Malhi et al., 2021). We focused on two regions: a Peruvian elevation transect with both NPP + GPP (n= 10, RAINFOR plot codes are ALP11, ALP30, SPD02, SPD01, TRU03, TRU08, TRU07, ESP01, WAY01, ACJ01(Malhi et al., 2017)) and a Bornean logging transect with only NPP (n= 11 RAINFOR plot codes are DAN-04, DAN-05, LAM-01, LAM-02, MLA-01, MLA-02, SAF-01, SAF-02, SAF-03, SAF-04, SAF-05 (Riutta et al., 2018). These plots were chosen because there are large changes in NPP/GPP across the elevation or logging gradient. GEDI data – We used the vertical forest structure (L2A and L2B, Version 2) and biomass (L4a) products from the GEDI instrument (R. Dubayah et al., 2020) between April 2019 to December 2022 for tropical forest regions (R. O. Dubayah et al., 2023). We used a quality filtering recipe developed in collaboration with GEDI Science Team members from the University of Maryland and NASA Goddard to identify the highest quality GEDI vegetation shots (R. Dubayah et al., 2022). A data layer that this iterative local outlier detection algorithm uses to exclude data is publicly available at R. O. Dubayah et al., (2023). For instance, some of the key data filters we applied were: included degrade flags of 0,3,8,10,13,18,20,23,28,30,33,38,40,43,48,60,63,68, L2A and L2B quality flags = 1 (only use highest quality data), sensitivity >= 0.98. With the GEDI data, we used canopy height, the height of median energy (HOME), and the number of canopy layers following Doughty et al 2023 (Doughty et al., 2023). Across all tropical forests, we created 300 by 300 m pixels containing all averaged (mean) GEDI data between 2019 and 2022. Using the centroid coordinates from each of the 2,102 plots, we found the 300 by 300 m averaged GEDI pixel that encompassed the plot. If the plot was not encompassed by the GEDI data, we searched a wider area by incrementally averaging a gradually increasing area of 1, 3, 5, and 10 pixels. In other words, if no 300 by 300 m pixel encompassed the plot, then we averaged all GEDI data an area one pixel out (4 by 4 = 1200 by 1200 m, 6 by 6 = 1800 by 1800m, 11 by 11 = 3300m by 3300m), gradually increasing the square until it encompassed an area with GEDI data. To compare with the NPP/GPP plots we compared RS trait and GEDI data for individual footprints within a 0.03 km radius of the plot coordinates. Remotely sensed leaf trait data – Based on a broader set of field campaigns, Aguirre-Gutiérrez et al., (2021) used Sentinel-2, climatic, topography, and soil data to create remotely sensed canopy trait maps for P=phosphorus % leaf concentration, WD = wood density g.cm-3, and LMA=Leaf mass area g m-2. Other data layers – We compared % one peak to several other climates, soils, leaf traits, and ecoregion maps listed below for the Amazon basin. Each dataset had its own resolution, which we standardized to 0.1 by 0.1 degrees. We used total cation exchange capacity (CEC) from soil grids (Batjes et al., 2020) from 0-5cm in units of mmol(c)/kg. We averaged TerraClimate (Abatzoglou et al., 2018) data between 2000 and 2018 for Vapor Pressure Deficit (VPD in kPa), Mean Monthly Precipitation (MMP) (mm/month), potential evapotranspiration (PET) and maximum and minimum temperature (°C). Statistical analysis – We used the Matlab (Matlab, MathWorks Inc., Natick, MA, USA) function “fitlm” to fit linear models to compare variables such as soil data, environmental data, leaf trait data (at 0.1° resolution) and GEDI structure data (300m and bigger resolution) to field biomass and NPP/GPP estimates. The P values listed are for the t-statistic of the two-sided hypothesis test. We used R to create a linear model to predict the best model ranked by AIC and parsimony using the dredge function from the MuMIn library (Bartoń, 2009). We also used the CAR package (Fox J & S, 2019) and the VIF command to test for multi-collinearity between variables. To account for spatial autocorrelation, we used Simultaneous Auto-Regressive (SARerr) models (F. Dormann et al., 2007) using the R library ‘spdep’ (Bivand, Hauke, & Kossowski, 2013). We tested different neighborhood distances from 10 km to 300 km and found that AIC was minimized at 80 km (Fig S3) and the corresponding correlogram showed reduced spatial autocorrelation (Fig S4). To predict leaf traits with the spectral information, we used the Partial Least Squares Regression (PLSR) (Geladi & Kowalski, 1986) using the PLSregress command in Matlab (Matlab, MathWorks Inc., Natick, MA, USA). To avoid over-fitting the number of latent factors, we minimized the mean square error with K-fold cross-validation. We use 70% of our data to calibrate our model and then the remaining 30% to test the accuracy of our model using r2. We use adjusted r2 which penalizes for small sample sizes throughout the manuscript. # Satellite-derived trait data slightly improves tropical forest biomass, NPP, and GPP predictions [https://doi.org/10.5061/dryad.ttdz08m5n](https://doi.org/10.5061/dryad.ttdz08m5n) The dataset contains leaf trait and spectral data to create Figures 1 and 2. It contains plot biomass data and satellite-derived leaf trait and structure data to create Figures 3-6. It contains plot NPP, GPP, and satellite-derived leaf trait and structure data to create Figures 7-8. ## Description of the data and file structure, including the associated Code/Software The Matlab code Finalcode_GEDIbiomass_Doughty2024.m contains all the code and data to create all the figures in the paper. The code has several sections that can be run independently. The first section starting on line 1 uses the dataset traitcompare.mat to create Figure 1. This dataset contains two tables of leaf trait data called carnegiechem and merged. Units and column names are contained within the table. The second section starting on line 62 uses the dataset traitgedidat.mat to create Figures 3-5 and Figures S1 and S2. This dataset contains a table called agball with plot biomass and coordinates. It also has the trait and GEDI data for these plots in nested structures. Units and descriptions are given in the code. The third section starting on line 443 uses the datasets traitgedidat.mat and soilclimdata.mat to create Figure 6. The dataset traitgedidat.mat is the same as described above and soilclimdata.mat contains 0.1 by 0.1 degree gridded data for climate variables like Tmax (C) or VPD (Pa) or soil chemistry like CEC. Units and description are given in the code. The forth section starting on line 536 uses the dataset Tamtreeheight.mat to create Figures 7 and 8. The dataset gedivsplot.mat contains table data with plot data, and nearby trait and GEDI data for several GEM plots. Units and description are given in the code and the tables. The fifth section starting on line 791 uses the dataset CombspecDBH.mat to create Figure 2. The dataset has variables specallz which is the leaf spectral data from 350-1075 nm for each leaf and dbhz1 with is the corresponding tree dbh (cm). It also has LMAz which is the LMA data with datz as the corresponding spectral data. To estimate spatial autocorrelation and the best model by AIC to create Table 1 and Figures S3 and 4, we used the R code processgedidata.r and the dataset biomass_trait_GEDI.xlsx. This dataset contains a table with latitude, longitude, field biomass and remote sensed biomass (Mg Ha-1), and traits LMA (g m2), Phosphorus (%), tree height (m), HOME (m) and % one peak (unitless). ## Sharing/Access information Original GEDI data are available from the USGS. Improving tropical forest biomass predictions can accurately value tropical forests for their ecosystem services. Recently, the Global Ecosystem Dynamics Investigation (GEDI) lidar was activated on the international space station (ISS) to improve biomass predictions by providing detailed 3D forest structure and height data. However, there is still debate on how best to predict tropical forest biomass using GEDI data. Here we compare GEDI predicted biomass to 2,102 tropical forest biomass plots and find that adding a remotely sensed (RS) trait map of LMA (Leaf Mass per Area) significantly (P<0.001) improves field biomass predictions, but by only a small amount (r2=0.01). However, it may also help reduce the bias of the residuals because, for instance, there was a negative relationship between both LMA (r2 of 0.34) and %P (r2=0.31) and residuals. This improvement in predictability corresponds with measurements from 523 individual trees where LMA predicts Diameter at Breast height (DBH) (the critical measurement underlying plot biomass) with an r2=0.04, and spectroscopy (400-1075 nm) predicts DBH with an r2=0.01. Adding environmental datasets may offer further improvements and max temperature (Tmax) predicts Amazonian biomass residuals with an r2 of 0.76 (N=66). Finally, for a network of net primary production (NPP) and gross primary production (GPP) plots (N=21), RS traits are better at predicting fluxes than structure variables like tree height or Height Of Median Energy (HOME). Overall, trait maps, especially future improved ones produced by surface biology geology (SBG), may improve biomass and carbon flux predictions by a small but significant amount.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Embargo end date: 06 Mar 2024Publisher:Dryad Field leaf trait and spectroscopy data – We used leaf trait and spectral data from an extensive field campaign along an elevation gradient (from 3500 m to 220 m elevation) in the Peruvian Amazon where leaf traits for 60-80% of basal area of trees >10cm DBH were measured within a well-studied 1 ha plot network from April – November 2013 (Enquist et al., 2017). In each one ha plot (N=10 plots), we sampled the most abundant species as determined through basal area weighting (enough species generally to cover ~80% of the plot’s basal area). For each species, we sampled the five (three in the lowlands) largest trees (based on diameter at breast height (DBH)) and sampled one sun and one shade branch. On each of these branches, leaf chemistry and leaf mass area (LMA) were measured with the methodology detailed in Asner et al. (2014). On five randomly selected leaves for each branch, we measured hemispherical reflectance with an ASD Fieldspec Handheld 2 with fiber optic cable, a contact probe that has its own calibrated light source, and a leaf clip (Analytical Spectral Devices High-Intensity Contact Probe and Leaf Clip, Boulder, Colorado, USA) following (Doughty et al., 2017). We measured leaf spectroscopy (400-1075 nm) on the same branches where the leaf traits were collected. Both LMA and Chlorophyll A had previously been shown with this dataset to have a correlation with leaf spectroscopy (Doughty et al., 2017). However, we had not previously tried to compare leaf spectral data with DBH directly. Plot data – Aboveground biomass - We used 2,102 of 19,160 total AGB field plots between +30° and -30° latitude classified as broadleaf evergreen trees by MODIS PFT using public data from Duncanson et al 2022 that was organized and publicly available through ORNL DAAC as an RDS (R data serialization) file. Distribution plots are shown in Fig S1 (AGB) and S2 (residuals). NPP and GPP - We also used 21, 1 ha plots where NPP and sometimes GPP were measured following the GEM protocol (Malhi et al., 2021). We focused on two regions: a Peruvian elevation transect with both NPP + GPP (n= 10, RAINFOR plot codes are ALP11, ALP30, SPD02, SPD01, TRU03, TRU08, TRU07, ESP01, WAY01, ACJ01(Malhi et al., 2017)) and a Bornean logging transect with only NPP (n= 11 RAINFOR plot codes are DAN-04, DAN-05, LAM-01, LAM-02, MLA-01, MLA-02, SAF-01, SAF-02, SAF-03, SAF-04, SAF-05 (Riutta et al., 2018). These plots were chosen because there are large changes in NPP/GPP across the elevation or logging gradient. GEDI data – We used the vertical forest structure (L2A and L2B, Version 2) and biomass (L4a) products from the GEDI instrument (R. Dubayah et al., 2020) between April 2019 to December 2022 for tropical forest regions (R. O. Dubayah et al., 2023). We used a quality filtering recipe developed in collaboration with GEDI Science Team members from the University of Maryland and NASA Goddard to identify the highest quality GEDI vegetation shots (R. Dubayah et al., 2022). A data layer that this iterative local outlier detection algorithm uses to exclude data is publicly available at R. O. Dubayah et al., (2023). For instance, some of the key data filters we applied were: included degrade flags of 0,3,8,10,13,18,20,23,28,30,33,38,40,43,48,60,63,68, L2A and L2B quality flags = 1 (only use highest quality data), sensitivity >= 0.98. With the GEDI data, we used canopy height, the height of median energy (HOME), and the number of canopy layers following Doughty et al 2023 (Doughty et al., 2023). Across all tropical forests, we created 300 by 300 m pixels containing all averaged (mean) GEDI data between 2019 and 2022. Using the centroid coordinates from each of the 2,102 plots, we found the 300 by 300 m averaged GEDI pixel that encompassed the plot. If the plot was not encompassed by the GEDI data, we searched a wider area by incrementally averaging a gradually increasing area of 1, 3, 5, and 10 pixels. In other words, if no 300 by 300 m pixel encompassed the plot, then we averaged all GEDI data an area one pixel out (4 by 4 = 1200 by 1200 m, 6 by 6 = 1800 by 1800m, 11 by 11 = 3300m by 3300m), gradually increasing the square until it encompassed an area with GEDI data. To compare with the NPP/GPP plots we compared RS trait and GEDI data for individual footprints within a 0.03 km radius of the plot coordinates. Remotely sensed leaf trait data – Based on a broader set of field campaigns, Aguirre-Gutiérrez et al., (2021) used Sentinel-2, climatic, topography, and soil data to create remotely sensed canopy trait maps for P=phosphorus % leaf concentration, WD = wood density g.cm-3, and LMA=Leaf mass area g m-2. Other data layers – We compared % one peak to several other climates, soils, leaf traits, and ecoregion maps listed below for the Amazon basin. Each dataset had its own resolution, which we standardized to 0.1 by 0.1 degrees. We used total cation exchange capacity (CEC) from soil grids (Batjes et al., 2020) from 0-5cm in units of mmol(c)/kg. We averaged TerraClimate (Abatzoglou et al., 2018) data between 2000 and 2018 for Vapor Pressure Deficit (VPD in kPa), Mean Monthly Precipitation (MMP) (mm/month), potential evapotranspiration (PET) and maximum and minimum temperature (°C). Statistical analysis – We used the Matlab (Matlab, MathWorks Inc., Natick, MA, USA) function “fitlm” to fit linear models to compare variables such as soil data, environmental data, leaf trait data (at 0.1° resolution) and GEDI structure data (300m and bigger resolution) to field biomass and NPP/GPP estimates. The P values listed are for the t-statistic of the two-sided hypothesis test. We used R to create a linear model to predict the best model ranked by AIC and parsimony using the dredge function from the MuMIn library (Bartoń, 2009). We also used the CAR package (Fox J & S, 2019) and the VIF command to test for multi-collinearity between variables. To account for spatial autocorrelation, we used Simultaneous Auto-Regressive (SARerr) models (F. Dormann et al., 2007) using the R library ‘spdep’ (Bivand, Hauke, & Kossowski, 2013). We tested different neighborhood distances from 10 km to 300 km and found that AIC was minimized at 80 km (Fig S3) and the corresponding correlogram showed reduced spatial autocorrelation (Fig S4). To predict leaf traits with the spectral information, we used the Partial Least Squares Regression (PLSR) (Geladi & Kowalski, 1986) using the PLSregress command in Matlab (Matlab, MathWorks Inc., Natick, MA, USA). To avoid over-fitting the number of latent factors, we minimized the mean square error with K-fold cross-validation. We use 70% of our data to calibrate our model and then the remaining 30% to test the accuracy of our model using r2. We use adjusted r2 which penalizes for small sample sizes throughout the manuscript. # Satellite-derived trait data slightly improves tropical forest biomass, NPP, and GPP predictions [https://doi.org/10.5061/dryad.ttdz08m5n](https://doi.org/10.5061/dryad.ttdz08m5n) The dataset contains leaf trait and spectral data to create Figures 1 and 2. It contains plot biomass data and satellite-derived leaf trait and structure data to create Figures 3-6. It contains plot NPP, GPP, and satellite-derived leaf trait and structure data to create Figures 7-8. ## Description of the data and file structure, including the associated Code/Software The Matlab code Finalcode_GEDIbiomass_Doughty2024.m contains all the code and data to create all the figures in the paper. The code has several sections that can be run independently. The first section starting on line 1 uses the dataset traitcompare.mat to create Figure 1. This dataset contains two tables of leaf trait data called carnegiechem and merged. Units and column names are contained within the table. The second section starting on line 62 uses the dataset traitgedidat.mat to create Figures 3-5 and Figures S1 and S2. This dataset contains a table called agball with plot biomass and coordinates. It also has the trait and GEDI data for these plots in nested structures. Units and descriptions are given in the code. The third section starting on line 443 uses the datasets traitgedidat.mat and soilclimdata.mat to create Figure 6. The dataset traitgedidat.mat is the same as described above and soilclimdata.mat contains 0.1 by 0.1 degree gridded data for climate variables like Tmax (C) or VPD (Pa) or soil chemistry like CEC. Units and description are given in the code. The forth section starting on line 536 uses the dataset Tamtreeheight.mat to create Figures 7 and 8. The dataset gedivsplot.mat contains table data with plot data, and nearby trait and GEDI data for several GEM plots. Units and description are given in the code and the tables. The fifth section starting on line 791 uses the dataset CombspecDBH.mat to create Figure 2. The dataset has variables specallz which is the leaf spectral data from 350-1075 nm for each leaf and dbhz1 with is the corresponding tree dbh (cm). It also has LMAz which is the LMA data with datz as the corresponding spectral data. To estimate spatial autocorrelation and the best model by AIC to create Table 1 and Figures S3 and 4, we used the R code processgedidata.r and the dataset biomass_trait_GEDI.xlsx. This dataset contains a table with latitude, longitude, field biomass and remote sensed biomass (Mg Ha-1), and traits LMA (g m2), Phosphorus (%), tree height (m), HOME (m) and % one peak (unitless). ## Sharing/Access information Original GEDI data are available from the USGS. Improving tropical forest biomass predictions can accurately value tropical forests for their ecosystem services. Recently, the Global Ecosystem Dynamics Investigation (GEDI) lidar was activated on the international space station (ISS) to improve biomass predictions by providing detailed 3D forest structure and height data. However, there is still debate on how best to predict tropical forest biomass using GEDI data. Here we compare GEDI predicted biomass to 2,102 tropical forest biomass plots and find that adding a remotely sensed (RS) trait map of LMA (Leaf Mass per Area) significantly (P<0.001) improves field biomass predictions, but by only a small amount (r2=0.01). However, it may also help reduce the bias of the residuals because, for instance, there was a negative relationship between both LMA (r2 of 0.34) and %P (r2=0.31) and residuals. This improvement in predictability corresponds with measurements from 523 individual trees where LMA predicts Diameter at Breast height (DBH) (the critical measurement underlying plot biomass) with an r2=0.04, and spectroscopy (400-1075 nm) predicts DBH with an r2=0.01. Adding environmental datasets may offer further improvements and max temperature (Tmax) predicts Amazonian biomass residuals with an r2 of 0.76 (N=66). Finally, for a network of net primary production (NPP) and gross primary production (GPP) plots (N=21), RS traits are better at predicting fluxes than structure variables like tree height or Height Of Median Energy (HOME). Overall, trait maps, especially future improved ones produced by surface biology geology (SBG), may improve biomass and carbon flux predictions by a small but significant amount.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 21 Oct 2022Publisher:Dryad Authors:Messerman, Arianne;
Clause, Adam; Gray, Levi; Krkošek, Martin; +4 AuthorsMesserman, Arianne
Messerman, Arianne in OpenAIREMesserman, Arianne;
Clause, Adam; Gray, Levi; Krkošek, Martin; Rollins, Hilary; Trenham, Peter; Shaffer, Bradley; Searcy, Christopher;Messerman, Arianne
Messerman, Arianne in OpenAIREAvailable files to conduct a Bayesian integral projection model (IPM) and population viability analysis (PVA) for the California tiger salamander (CTS) include: -The preliminary frequentist CTS IPM and PVA script created by Christopher A. Searcy is: "CTS-Frequentist-IPM.R" -- Frequentist code to build the IPM and run the PVA. -The primary scripts to run the IPM and PVA are: "CTS_SOURCE.R" -- Source code for the vital rate functions. "CTS_IPM_PVA.R' -- Code to build the IPM, conduct sensitivity and elasticity analyses, and run the PVA. -The scripts used to build the vital rate functions that inform the IPM are: "CTS_Best_Survival.R" -- The best Cormack-Jolly-Seber model of metamorph and juvenile/adult CTS survival and recapture probabilities. "CTS_Growth.R" -- CTS metamorph and juvenile/adult growth functions. "CTS_Fertility.R" -- CTS fertility function. "CTS_Maturity.R" -- CTS maturity function. "CTS_Larval_Survival.R" -- CTS larval survival given egg density function. "CTS_Females_Precip.R" -- The proportion of CTS females breeding given annual December-January precipitation function. "CTS_Replacement.R" -- Adult-only replacement and reproductive success functions to construct piecewise environmental-dependency function. -All necessary data files to run the CTS_IPM_PVA.R script and support the findings of our study are: "adults-v2.txt" -- The adult CTS capture histories from the capture-mark-recapture study at Jepson prairie Preserve, CA. "covariates-v2.txt" -- The CTS capture histories from the capture-mark-recapture study at Jepson prairie Preserve, CA specifying individual body masses (ln-transformed; g). "metamorphs-v2.txt" -- The metamorph CTS capture histories from the capture-mark-recapture study at Jepson prairie Preserve, CA. "precip.csv" -- Study rain year-specific November-February and October-June precipitation values (mm). "density-distance.csv" -- Proportion of the post-metamorphic life stage individuals (from 0 to 1) found within 100-m distance radius increments from the pond shoreline. "Larval-Survival-Density.csv" -- Ln-transformed larval survival and Ln-transformed egg density (eggs/m^3) data. "meta-size-by-egg-density.csv" -- Study year-specific metamorph size and egg density (eggs/m^3) data. "Olcott20**.txt" -- Body size distributions of each cohort of CTS from the mark-recapture study across the 122 body size bins, where "**" indicates study year in the file name. "Stochastic_Climate_Pool.txt" -- Historic precipitation record from 1893-2012 (Vacaville and Nut Tree Airport station records). "Stochastic_Climate_Pool_Rev.txt" -- Historic precipitation record from 1893-2008 (Nut Tree Airport-only station records). "females-precip-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distribution of the function of female CTS breeding given December-January precipitation (mm). "fertility-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distribution of the function of clutch size (# eggs) given female CTS body mass (g). "growth-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distributions of the life stage-specific growth functions. "larval-survival-posterior-samples.csv" -- The 500 random samples from the posterior distribution of the function of larval survival probability given egg density (eggs/m^3). "maturity-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distribution of the function of maturity probabilitygiven CTS body mass (g). "replace-success-infection-samples-CENTERED.csv" -- The 500 random samples of the inflection point from the posterior distribution of the function of probability of metamorph recruitment being above the replacement rate given October-June precipitation (mm). "repro-success-infection-samples-CENTERED.csv" -- The 500 random samples of the inflection point from the posterior distribution of the function of probability of reproductive success given October-June precipitation (mm). "survival-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distribution of the Cormack-Jolly-Seber model of life stage-specific survival probabilities given body mass (g). Scripts were developed and run using R version 4.0.0. Integral projection models (IPMs) can estimate the population dynamics of species for which both discrete life stages and continuous variables influence demographic rates. Stochastic IPMs for imperiled species, in turn, can facilitate population viability analyses (PVAs) to guide conservation decision-making. Biphasic amphibians are globally distributed, often highly imperiled, and ecologically well-suited to the IPM approach. Herein, we present the first stochastic size- and stage-structured IPM for a biphasic amphibian, the U.S. federally threatened California tiger salamander (Ambystoma californiense; CTS). This Bayesian model reveals that CTS population dynamics show the greatest elasticity to changes in juvenile and metamorph growth and that populations are likely to experience rapid growth at low density. We integrated this IPM with climatic drivers of CTS demography to develop a PVA and examined CTS extinction risk under the primary threats of habitat loss and climate change. The PVA indicates that long-term viability is possible with surprisingly high (20–50%) terrestrial mortality, but simultaneously identified likely minimum terrestrial buffer requirements of 600–1000 m while accounting for numerous parameter uncertainties through the Bayesian framework. These analyses underscore the value of stochastic and Bayesian IPMs for understanding both climate-dependent taxa and those with cryptic life histories (e.g., biphasic amphibians) in service of ecological discovery and biodiversity conservation. In addition to providing guidance for CTS recovery, the contributed IPM and PVA supply a framework for applying these tools to investigations of ecologically-similar species. Please see the associated manuscript for full methodological details.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 21 Oct 2022Publisher:Dryad Authors:Messerman, Arianne;
Clause, Adam; Gray, Levi; Krkošek, Martin; +4 AuthorsMesserman, Arianne
Messerman, Arianne in OpenAIREMesserman, Arianne;
Clause, Adam; Gray, Levi; Krkošek, Martin; Rollins, Hilary; Trenham, Peter; Shaffer, Bradley; Searcy, Christopher;Messerman, Arianne
Messerman, Arianne in OpenAIREAvailable files to conduct a Bayesian integral projection model (IPM) and population viability analysis (PVA) for the California tiger salamander (CTS) include: -The preliminary frequentist CTS IPM and PVA script created by Christopher A. Searcy is: "CTS-Frequentist-IPM.R" -- Frequentist code to build the IPM and run the PVA. -The primary scripts to run the IPM and PVA are: "CTS_SOURCE.R" -- Source code for the vital rate functions. "CTS_IPM_PVA.R' -- Code to build the IPM, conduct sensitivity and elasticity analyses, and run the PVA. -The scripts used to build the vital rate functions that inform the IPM are: "CTS_Best_Survival.R" -- The best Cormack-Jolly-Seber model of metamorph and juvenile/adult CTS survival and recapture probabilities. "CTS_Growth.R" -- CTS metamorph and juvenile/adult growth functions. "CTS_Fertility.R" -- CTS fertility function. "CTS_Maturity.R" -- CTS maturity function. "CTS_Larval_Survival.R" -- CTS larval survival given egg density function. "CTS_Females_Precip.R" -- The proportion of CTS females breeding given annual December-January precipitation function. "CTS_Replacement.R" -- Adult-only replacement and reproductive success functions to construct piecewise environmental-dependency function. -All necessary data files to run the CTS_IPM_PVA.R script and support the findings of our study are: "adults-v2.txt" -- The adult CTS capture histories from the capture-mark-recapture study at Jepson prairie Preserve, CA. "covariates-v2.txt" -- The CTS capture histories from the capture-mark-recapture study at Jepson prairie Preserve, CA specifying individual body masses (ln-transformed; g). "metamorphs-v2.txt" -- The metamorph CTS capture histories from the capture-mark-recapture study at Jepson prairie Preserve, CA. "precip.csv" -- Study rain year-specific November-February and October-June precipitation values (mm). "density-distance.csv" -- Proportion of the post-metamorphic life stage individuals (from 0 to 1) found within 100-m distance radius increments from the pond shoreline. "Larval-Survival-Density.csv" -- Ln-transformed larval survival and Ln-transformed egg density (eggs/m^3) data. "meta-size-by-egg-density.csv" -- Study year-specific metamorph size and egg density (eggs/m^3) data. "Olcott20**.txt" -- Body size distributions of each cohort of CTS from the mark-recapture study across the 122 body size bins, where "**" indicates study year in the file name. "Stochastic_Climate_Pool.txt" -- Historic precipitation record from 1893-2012 (Vacaville and Nut Tree Airport station records). "Stochastic_Climate_Pool_Rev.txt" -- Historic precipitation record from 1893-2008 (Nut Tree Airport-only station records). "females-precip-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distribution of the function of female CTS breeding given December-January precipitation (mm). "fertility-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distribution of the function of clutch size (# eggs) given female CTS body mass (g). "growth-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distributions of the life stage-specific growth functions. "larval-survival-posterior-samples.csv" -- The 500 random samples from the posterior distribution of the function of larval survival probability given egg density (eggs/m^3). "maturity-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distribution of the function of maturity probabilitygiven CTS body mass (g). "replace-success-infection-samples-CENTERED.csv" -- The 500 random samples of the inflection point from the posterior distribution of the function of probability of metamorph recruitment being above the replacement rate given October-June precipitation (mm). "repro-success-infection-samples-CENTERED.csv" -- The 500 random samples of the inflection point from the posterior distribution of the function of probability of reproductive success given October-June precipitation (mm). "survival-posterior-samples-CENTERED.csv" -- The 500 random samples from the posterior distribution of the Cormack-Jolly-Seber model of life stage-specific survival probabilities given body mass (g). Scripts were developed and run using R version 4.0.0. Integral projection models (IPMs) can estimate the population dynamics of species for which both discrete life stages and continuous variables influence demographic rates. Stochastic IPMs for imperiled species, in turn, can facilitate population viability analyses (PVAs) to guide conservation decision-making. Biphasic amphibians are globally distributed, often highly imperiled, and ecologically well-suited to the IPM approach. Herein, we present the first stochastic size- and stage-structured IPM for a biphasic amphibian, the U.S. federally threatened California tiger salamander (Ambystoma californiense; CTS). This Bayesian model reveals that CTS population dynamics show the greatest elasticity to changes in juvenile and metamorph growth and that populations are likely to experience rapid growth at low density. We integrated this IPM with climatic drivers of CTS demography to develop a PVA and examined CTS extinction risk under the primary threats of habitat loss and climate change. The PVA indicates that long-term viability is possible with surprisingly high (20–50%) terrestrial mortality, but simultaneously identified likely minimum terrestrial buffer requirements of 600–1000 m while accounting for numerous parameter uncertainties through the Bayesian framework. These analyses underscore the value of stochastic and Bayesian IPMs for understanding both climate-dependent taxa and those with cryptic life histories (e.g., biphasic amphibians) in service of ecological discovery and biodiversity conservation. In addition to providing guidance for CTS recovery, the contributed IPM and PVA supply a framework for applying these tools to investigations of ecologically-similar species. Please see the associated manuscript for full methodological details.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 17 May 2023Publisher:Dryad Authors:Doughty, Christopher;
Crous, Kristine; Rey-Sanchez, Camillo; Carter, Kelsey; +1 AuthorsDoughty, Christopher
Doughty, Christopher in OpenAIREDoughty, Christopher;
Crous, Kristine; Rey-Sanchez, Camillo; Carter, Kelsey; Fauset, Sophie;Doughty, Christopher
Doughty, Christopher in OpenAIREField Data - We estimate canopy temperature at the km 83 eddy covariance tower in the Tapajos region of Brazil 1–3 using a pyrgeometer (Kipp and Zonen, Delft, Netherlands) mounted at 64 m to measure upwelling longwave radiation (L↑ in W m-2) with an estimated radiative-flux footprint of 8,000 m2 4. Data were collected every 2 seconds and averaged over 30-minute intervals between August 2001 and March 2004. We estimated canopy temperature with the following equation: Eq 1 – Canopy temperature (°C) = (L↑/(E*5.67e-8))0.25-273.15 We chose an emissivity value (E) of 0.98 for the tower data, as this was the most common value used in the ECOSTRESS data (SDS_Emis1-5 (ECO2LSTE.001) and the broader literature for tropical forests 5. We compared canopy temperature derived from the pyrgeometer to eddy covariance derived latent heat fluxes (flux footprint ~1 km2), air temperature at 40 m, which is the approximate canopy height (model 076B, Met One, Oregon, USA; and model 107, Campbell Scientific, Logan, Utah, USA) and soil moisture at depths of 40 cm (model CS615, Campbell Scientific, Logan, Utah, USA). Further details on instrumentation and eddy covariance processing can be found in 1,3. This site was selectively logged, which had a minor overall impact on the forest 6, but did not affect any trees near the tower. Leaf thermocouple data - We measured canopy leaf temperature at a 30 m canopy walk-up tower between July to December of 2004 and July to December of 2005 at the same site. We initially placed 50 thermocouples on canopy-exposed leaves of Sextonia rubra, Micropholis sp., Lecythis lurida) (originally published in Doughty and Goulden 2008). Fine wire thermocouples (copper constantan 0.005 Omega, Stamford, CT) were attached to the underside of leaves by threading the wire through the leaf and inserting the end of the thermocouple into the abaxial surface. The thermocouples were wired into a multiplexer attached to a data logger (models AM25T and 23X, Campbell Scientific, Logan, UT, USA) and the data were recorded at 1 Hz. Additional upper-canopy leaf thermocouple data from Brazil7, Puerto Rico8, Panama9, Atlantic forest Brazil10 and Australia 11, were generally collected in a similar manner. Satellite data - ECOSTRESS data (ECO2LSTE.001) – The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission is a thermal infrared (TIR) multispectral scanner with five spectral bands at 8.28, 8.63, 9.07, 10.6, and 12.05 µm. The sensor has a native spatial resolution of 38 m x 68 m, resampled to 70 m x 70 m, and a swath width of 402 km (53°). Data are collected from an average altitude of 400 ± 25 km on the International Space Station (ISS). ECOSTRESS is an improvement over other thermal sensors because no other sensors provide TIR data with sufficient spatial, temporal, and spectral resolution to reliably estimate LST at the local-to-global scale for a diurnal cycle 12. To ensure the highest quality data, we used ECOSTRESS quality flag 3520, which identifies the best quality pixels (no cloud detected), a minimum-maximum difference (MMD) indicative of vegetation or water (Kealy and Hook 1993), and nominal atmospheric opacity. We accessed ECOSTRESS LST data through the AppEEARS website (https://lpdaac.usgs.gov/tools/appeears/) for the following products and periods: SDS_LST (ECO2LSTE.001) from a long longitudinal swath of the Amazon for 25 December 2018 to 20 July 2020 (SI Fig 1a red box) and then a larger area of the western Amazon for 18 September to 29 September 2019 (SI Fig 1a green box), Central Africa for 1 August to 30 August 2019 (SI Fig 1b), and SE Asia for 15 January to 30 February 2020 (SI Fig. 1c). The dates were chosen as all ECOSTRESS data available at the start of the study for the smaller regions and for warm periods with low soil moisture for the larger areas. We calculated “peak median,” which is defined as the average of the highest three medians of each granule (i.e., for the Amazon SI Fig. 1a, there were 934 granules) for each hour period. Comparison of LST data – We compared ECOSTRESS LST to VIIRS LST (VNP21A1D.001) and MODIS LST (MYD11A1.006). A more detailed comparison and description of these sensors can be found in Hulley et al 202113. Details for the sensors and quality flags used are given in Table S1. Broadly, G1 for ECOSTRESS and VIIRS is classified as vegetation (using emissivity) and of medium quality. G2 is classified as vegetation, but of the highest quality. MODIS landcover classifies this region as almost entirely broadleaf evergreen vegetation, but using MMD (emissivity) only 18% (VIIRS) and 12% (ECOSTRESS) of the data are classified as vegetation, rather than as soils and rocks (Table S2). Therefore, we use the vegetation classification (from MMD) as a very conservative estimate of complete forest canopy cover and not farms, urban, or degraded forest where rocks or soils are more likely to appear to satellites. SMAP data – To estimate pantropical soil moisture, we use the Soil Moisture Active Passive (SMAP) sensor and the product Geophysical_Data_sm_rootzone (SPL4SMGP.005). SMAP measurements provide remote sensing of soil moisture in the top 5 cm of the soil 14 and the L4 products combine SMAP observations and complementary information from a variety of sources. We accessed SMAP data from the AppEEARS website for the following products and periods: Amazon for 25 December 2018 to 20 July 2020 (SI Fig 1a), Central Africa for 25 December 2019 to 20 July 2020 (SI Fig 1b), and Borneo for 25 December 2018 to 20 July 2020 (SI Fig 1c). Warming experiments – For model validation, we used the results of three upper-canopy leaf and branch warming experiments of 2°C (Brazil), 3°C (Puerto Rico), and 4°C (Australia). The first experiment (Brazil), was 4 individual leaf-resistant heaters on each of 6 different upper-canopy species at the Floresta National (FLONA) do Tapajos as part of the Large-Scale Biosphere–Atmosphere Ecology Program (LBA-ECO) in Santarem, Brazil14. On the same six species, black plastic passively heated branches by an average ~2°C. Initially, heat balance sap flow sensors and the passive heaters were added to 40 branches, but we had confidence in the data from 9 heated and 4 control in the final analysis. The second experiment (Puerto Rico) had two species (Ocotea sintenisii (Mez) Alain and Guarea guidonia (L.) Sleumer where leaves were heated by 3 °C at the Tropical Responses to Altered Climate Experiment (TRACE) canopy tower site at Sabana Field Research Station, Luquillo, Puerto Rico8. The final experiment (Australia), which increased leaf temperatures by 4 °C, was conducted at Daintree Rainforest Observatory (DRO) in Cape Tribulation, Far North Queensland, Australia. Leaf heaters were installed using a pair of 30-gauge copper-constantan thermocouples, one reference leaf, and one heated with a target temperature differential of 4°C. There were two pairs in the upper canopy of each tree crown installed in 2–3 individuals across four species with the thermocouples installed on the underside of the leaves. Two absolute 36-gauge copper-constantan thermocouples were installed in each species to measure the leaf temperatures of the reference leaves. Thermocouple wires connected into an AM25T multiplexer from Campbell Scientific connected to a CR1000 Campbell datalogger. More details about the experiment and sensors can be found in 11. Model – We created a model of individual leaves on a tree (100 by 100 grid where each leaf is a pixel) to estimate the upper limit of tropical canopy temperatures with projected changes in climate. At the start of the simulation, we randomly applied the measured distribution (ambient Fig 1c) of canopy leaf temperatures >31.2 °C (chosen to give a mean canopy temperature of 33.2 ± 0.4 °C, matching the canopy average Fig 1b) to the entire grid. Each year we increased the mean air temperatures by 0.03°C to simulate a warming planet. As air temperatures reached +2, 3, and 4°C, we applied the leaf temperature distributions (but subtracted out the air temperature increases) from the different warming experiments (+2°C (Brazil), +3°C (Puerto Rico), and +4°C (Australia), respectively (Fig S7)). We ran the model at a daily time step with leaves flushing once a year (all dead leaves reset to living each year). In addition, to take into account the effect of climate inter-annual variation - specifically drought, these mean canopy temperatures were further increased or decreased by deviations from mean maximum air temperatures at 40 m pulled each day from the Tapajos eddy covariance tower1–3 and soil moisture at 40 cm depth (m3 m-3) which controlled canopy temperatures following equation 2 (Fig S6). Eq 2 – Canopy temperature (°C) = 46.5-33.6*soil moisture (m3 m-3) For example, in a non-drought year, on a day when max air temperatures were 0.1 °C higher than average and soil moisture was 0.01 m3 m-3 lower than average (which would add 0.3 °C to canopy temperatures (Eq 2)), we would add 0.4 °C to the grid canopy temperature that day. Every year, there was a 10% random probability of either a minor (80% probability) drought which reduced soil moisture by 0.1 m3 m-3 and increased air temperatures by 0.5 °C or severe drought (20% probability), which reduced soil moisture by 0.2 m3 m-3 and increased air temperatures by 1 °C. This is similar to the Amazon-wide temperature increases during the last El Niño 15. If an individual leaf temperature increases to above 46.7 °C (Tcrit) the leaf died, following Slot et al. (2021). Prior research has suggested that irreversible damage could begin at 45 °C 16 and T50 for tropical species is 49.9 °C 17, and we use these values in a sensitivity study. We further explore the impact of duration of Tcrit on mortality in a sensitivity study (ranging between needing a single exposure to four exposures to Tcrit to die). Over the season, if a leaf died, then it did not contribute towards canopy evapotranspiration. We ran simulations as a 3D canopy with an LAI of 5 where if the top leaf died, then it was replaced by a shade-adapted leaf with a Tcrit 1 °C lower 18. If each of the 5 LAIs died, then all leaves in that grid cell were dead and canopy evaporative cooling decreased by that percentage. Several lines of evidence suggest that under normal hydraulic conditions, when radiation load increases from ~350 to 1100 W m-2 (e.g. between shady and sunny conditions) average canopy temperature increases by ~3 °C and therefore, evaporative cooling for a full 1100 W m-2 is ~4.4°C4,19 (we vary this in a sensitivity study between 3.7 and 5.1°C). For example, if, over a year, 1000 leaves (10% of all leaves) surpass Tcrit and die, evaporative cooling for all leaves in the grid will be reduced by 10% (1000/(100 by 100 grid)) or 0.44 °C and 0.44 °C will be added to mean canopy temperature. Therefore, mean canopy temperature could heat up by a maximum of 4.4°C either due to a reduction of soil moisture or from an increase in dead leaves. We ran each simulation until the point where all leaves were dead and repeated this 30 times. We assumed loss of tree function following the death of all leaves, but we discuss this further in the discussion. We then ran sensitivity studies for several of the key variables (bold indicates the standard model parameter) including: drought (0.05, 0.1, to 0.2 m3 m-3 decrease in soil moisture), change in Tcrit (Tcrit: 45, 46.7, 49.9 °C), Tcrit range (100 by 100 grid =random distribution of 46.7±2, 100 by 100 grid =46.7±0), Max evaporative cooling (3.7, 4.4°C), (Tcrit duration (exceed Tcrit once, exceed Tcrit more than 3 times) and soil moisture coefficient (-33.6 -38.2; i.e. change the slope from Fig S6 by ± 1 sd). Methods References Miller, S. D. et al. Biometric and micrometeorological measurements of tropical forest carbon balance. Ecol. Appl. 14, 114–126 (2004). da Rocha, H. R. et al. Seasonality of water and heat fluxes over a tropical forest in eastern Amazonia. Ecol. Appl. 14, 22–32 (2004). Goulden, M. L. et al. Diel and seasonal patterns of tropical forest co2 exchange. Ecol. Appl. 14, 42–54 (2004). Kivalov, S. N. & Fitzjarrald, D. R. Observing the Whole-Canopy Short-Term Dynamic Response to Natural Step Changes in Incident Light: Characteristics of Tropical and Temperate Forests. Boundary-Layer Meteorol. 173, 1–52 (2019). Jin, M. & Liang, S. An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations. J. Clim. 19, (2006). Miller, S. D. et al. Reduced impact logging minimally alters tropical rainforest carbon and energy exchange. Proc. Natl. Acad. Sci. 108, 19431 LP – 19435 (2011). Doughty, C. E. An In Situ Leaf and Branch Warming Experiment in the Amazon. Biotropica 43, 658–665 (2011). Carter, K. R., Wood, T. E., Reed, S. C., Butts, K. M. & Cavaleri, M. A. Experimental warming across a tropical forest canopy height gradient reveals minimal photosynthetic and respiratory acclimation. Plant. Cell Environ. 44, 2879–2897 (2021). Rey-Sanchez, A. C., Slot, M., Posada, J. & Kitajima, K. Spatial and seasonal variation of leaf temperature within the canopy of a tropical forest. Clim. Res. 71, 75–89 (2016). Fauset, S. et al. Differences in leaf thermoregulation and water use strategies between three co-occurring Atlantic forest tree species. Plant. Cell Environ. 41, 1618–1631 (2018). Crous K Y, A W Cheesman, K Middleby, Rogers Eie, A Wujeska-Klause, A Y M Bouet, D S Ellsworth, M J Liddell, L A Cernusak, C V M Barton, Similar patterns of leaf temperatures and thermal acclimation to warming in temperate and tropical tree canopies., Tree Physiology, 2023;, tpad054, https://doi.org/10.1093/treephys/tpad054. Xiao, J., Fisher, J. B., Hashimoto, H., Ichii, K. & Parazoo, N. C. Emerging satellite observations for diurnal cycling of ecosystem processes. Nat. Plants 7, 877–887 (2021). Hulley, G. C. et al. Validation and Quality Assessment of the ECOSTRESS Level-2 Land Surface Temperature and Emissivity Product. IEEE Trans. Geosci. Remote Sens. 60, 1–23 (2022). Reichle, R., Lannoy, G. De, Koster, R. D., Crow, W. T. & 2017., J. S. K. SMAP L4 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 3. Boulder, Color. USA. NASA Natl. Snow Ice Data Cent. Distrib. Act. Arch. Center. doi https//doi.org/10.5067/B59DT1D5UMB4. (2017). Jiménez-Muñoz, J. C. et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 6, 33130 (2016). Berry, J. & Bjorkman, O. Photosynthetic Response and Adaptation to Temperature in Higher Plants. Annu. Rev. Plant Physiol. 31, 491–543 (1980). Slot, M. et al. Leaf heat tolerance of 147 tropical forest species varies with elevation and leaf functional traits, but not with phylogeny. Plant. Cell Environ. 44, (2021). Slot, M., Krause, G. H., Krause, B., Hernández, G. G. & Winter, K. Photosynthetic heat tolerance of shade and sun leaves of three tropical tree species. Photosynth. Res. 141, 119–130 (2019). Doughty, C. E. & Goulden, M. L. Are tropical forests near a high temperature threshold? J. Geophys. Res. Biogeosciences (2009) doi:10.1029/2007JG000632. The critical temperature beyond which photosynthetic machinery in tropical trees begins to fail averages ~46.7°C (Tcrit) 1. However, it remains unclear whether leaf temperatures experienced by tropical vegetation approach this threshold or soon will under climate change. We found that pantropical canopy temperatures independently triangulated from individual leaf thermocouples, pyrgeometers, and remote sensing (ECOSTRESS) have midday-peak temperatures of ~34°C during dry periods, with a long high-temperature tail that can exceed 40°C. Leaf thermocouple data from multiple sites across the tropics suggest that even within pixels of moderate temperatures, upper-canopy leaves exceed Tcrit 0.01% of the time. Further, upper-canopy leaf warming experiments (+2, 3, and 4°C in Brazil, Puerto Rico, and Australia) increased leaf temperatures non-linearly with peak leaf temperatures exceeding Tcrit 1.3% of the time (11% >43.5°C, 0.3% >49.9°C). Using an empirical model incorporating these dynamics (validated with warming experiment data), we found that tropical forests can withstand up to a 3.9 ± 0.5 °C increase in air temperatures before a potential collapse in metabolic function, but the remaining uncertainty in our understanding of Tcrit could reduce this to 2.6 ± 0.6°C. The 4.0°C estimate is within the “worst case scenario” (RCP-8.5) of climate change predictions2 for tropical forests and therefore it is still within our power to decide (e.g., by not taking the RCP 8.5 route) the fate of these critical realms of carbon, water, and biodiversity 3,4.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 17 May 2023Publisher:Dryad Authors:Doughty, Christopher;
Crous, Kristine; Rey-Sanchez, Camillo; Carter, Kelsey; +1 AuthorsDoughty, Christopher
Doughty, Christopher in OpenAIREDoughty, Christopher;
Crous, Kristine; Rey-Sanchez, Camillo; Carter, Kelsey; Fauset, Sophie;Doughty, Christopher
Doughty, Christopher in OpenAIREField Data - We estimate canopy temperature at the km 83 eddy covariance tower in the Tapajos region of Brazil 1–3 using a pyrgeometer (Kipp and Zonen, Delft, Netherlands) mounted at 64 m to measure upwelling longwave radiation (L↑ in W m-2) with an estimated radiative-flux footprint of 8,000 m2 4. Data were collected every 2 seconds and averaged over 30-minute intervals between August 2001 and March 2004. We estimated canopy temperature with the following equation: Eq 1 – Canopy temperature (°C) = (L↑/(E*5.67e-8))0.25-273.15 We chose an emissivity value (E) of 0.98 for the tower data, as this was the most common value used in the ECOSTRESS data (SDS_Emis1-5 (ECO2LSTE.001) and the broader literature for tropical forests 5. We compared canopy temperature derived from the pyrgeometer to eddy covariance derived latent heat fluxes (flux footprint ~1 km2), air temperature at 40 m, which is the approximate canopy height (model 076B, Met One, Oregon, USA; and model 107, Campbell Scientific, Logan, Utah, USA) and soil moisture at depths of 40 cm (model CS615, Campbell Scientific, Logan, Utah, USA). Further details on instrumentation and eddy covariance processing can be found in 1,3. This site was selectively logged, which had a minor overall impact on the forest 6, but did not affect any trees near the tower. Leaf thermocouple data - We measured canopy leaf temperature at a 30 m canopy walk-up tower between July to December of 2004 and July to December of 2005 at the same site. We initially placed 50 thermocouples on canopy-exposed leaves of Sextonia rubra, Micropholis sp., Lecythis lurida) (originally published in Doughty and Goulden 2008). Fine wire thermocouples (copper constantan 0.005 Omega, Stamford, CT) were attached to the underside of leaves by threading the wire through the leaf and inserting the end of the thermocouple into the abaxial surface. The thermocouples were wired into a multiplexer attached to a data logger (models AM25T and 23X, Campbell Scientific, Logan, UT, USA) and the data were recorded at 1 Hz. Additional upper-canopy leaf thermocouple data from Brazil7, Puerto Rico8, Panama9, Atlantic forest Brazil10 and Australia 11, were generally collected in a similar manner. Satellite data - ECOSTRESS data (ECO2LSTE.001) – The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission is a thermal infrared (TIR) multispectral scanner with five spectral bands at 8.28, 8.63, 9.07, 10.6, and 12.05 µm. The sensor has a native spatial resolution of 38 m x 68 m, resampled to 70 m x 70 m, and a swath width of 402 km (53°). Data are collected from an average altitude of 400 ± 25 km on the International Space Station (ISS). ECOSTRESS is an improvement over other thermal sensors because no other sensors provide TIR data with sufficient spatial, temporal, and spectral resolution to reliably estimate LST at the local-to-global scale for a diurnal cycle 12. To ensure the highest quality data, we used ECOSTRESS quality flag 3520, which identifies the best quality pixels (no cloud detected), a minimum-maximum difference (MMD) indicative of vegetation or water (Kealy and Hook 1993), and nominal atmospheric opacity. We accessed ECOSTRESS LST data through the AppEEARS website (https://lpdaac.usgs.gov/tools/appeears/) for the following products and periods: SDS_LST (ECO2LSTE.001) from a long longitudinal swath of the Amazon for 25 December 2018 to 20 July 2020 (SI Fig 1a red box) and then a larger area of the western Amazon for 18 September to 29 September 2019 (SI Fig 1a green box), Central Africa for 1 August to 30 August 2019 (SI Fig 1b), and SE Asia for 15 January to 30 February 2020 (SI Fig. 1c). The dates were chosen as all ECOSTRESS data available at the start of the study for the smaller regions and for warm periods with low soil moisture for the larger areas. We calculated “peak median,” which is defined as the average of the highest three medians of each granule (i.e., for the Amazon SI Fig. 1a, there were 934 granules) for each hour period. Comparison of LST data – We compared ECOSTRESS LST to VIIRS LST (VNP21A1D.001) and MODIS LST (MYD11A1.006). A more detailed comparison and description of these sensors can be found in Hulley et al 202113. Details for the sensors and quality flags used are given in Table S1. Broadly, G1 for ECOSTRESS and VIIRS is classified as vegetation (using emissivity) and of medium quality. G2 is classified as vegetation, but of the highest quality. MODIS landcover classifies this region as almost entirely broadleaf evergreen vegetation, but using MMD (emissivity) only 18% (VIIRS) and 12% (ECOSTRESS) of the data are classified as vegetation, rather than as soils and rocks (Table S2). Therefore, we use the vegetation classification (from MMD) as a very conservative estimate of complete forest canopy cover and not farms, urban, or degraded forest where rocks or soils are more likely to appear to satellites. SMAP data – To estimate pantropical soil moisture, we use the Soil Moisture Active Passive (SMAP) sensor and the product Geophysical_Data_sm_rootzone (SPL4SMGP.005). SMAP measurements provide remote sensing of soil moisture in the top 5 cm of the soil 14 and the L4 products combine SMAP observations and complementary information from a variety of sources. We accessed SMAP data from the AppEEARS website for the following products and periods: Amazon for 25 December 2018 to 20 July 2020 (SI Fig 1a), Central Africa for 25 December 2019 to 20 July 2020 (SI Fig 1b), and Borneo for 25 December 2018 to 20 July 2020 (SI Fig 1c). Warming experiments – For model validation, we used the results of three upper-canopy leaf and branch warming experiments of 2°C (Brazil), 3°C (Puerto Rico), and 4°C (Australia). The first experiment (Brazil), was 4 individual leaf-resistant heaters on each of 6 different upper-canopy species at the Floresta National (FLONA) do Tapajos as part of the Large-Scale Biosphere–Atmosphere Ecology Program (LBA-ECO) in Santarem, Brazil14. On the same six species, black plastic passively heated branches by an average ~2°C. Initially, heat balance sap flow sensors and the passive heaters were added to 40 branches, but we had confidence in the data from 9 heated and 4 control in the final analysis. The second experiment (Puerto Rico) had two species (Ocotea sintenisii (Mez) Alain and Guarea guidonia (L.) Sleumer where leaves were heated by 3 °C at the Tropical Responses to Altered Climate Experiment (TRACE) canopy tower site at Sabana Field Research Station, Luquillo, Puerto Rico8. The final experiment (Australia), which increased leaf temperatures by 4 °C, was conducted at Daintree Rainforest Observatory (DRO) in Cape Tribulation, Far North Queensland, Australia. Leaf heaters were installed using a pair of 30-gauge copper-constantan thermocouples, one reference leaf, and one heated with a target temperature differential of 4°C. There were two pairs in the upper canopy of each tree crown installed in 2–3 individuals across four species with the thermocouples installed on the underside of the leaves. Two absolute 36-gauge copper-constantan thermocouples were installed in each species to measure the leaf temperatures of the reference leaves. Thermocouple wires connected into an AM25T multiplexer from Campbell Scientific connected to a CR1000 Campbell datalogger. More details about the experiment and sensors can be found in 11. Model – We created a model of individual leaves on a tree (100 by 100 grid where each leaf is a pixel) to estimate the upper limit of tropical canopy temperatures with projected changes in climate. At the start of the simulation, we randomly applied the measured distribution (ambient Fig 1c) of canopy leaf temperatures >31.2 °C (chosen to give a mean canopy temperature of 33.2 ± 0.4 °C, matching the canopy average Fig 1b) to the entire grid. Each year we increased the mean air temperatures by 0.03°C to simulate a warming planet. As air temperatures reached +2, 3, and 4°C, we applied the leaf temperature distributions (but subtracted out the air temperature increases) from the different warming experiments (+2°C (Brazil), +3°C (Puerto Rico), and +4°C (Australia), respectively (Fig S7)). We ran the model at a daily time step with leaves flushing once a year (all dead leaves reset to living each year). In addition, to take into account the effect of climate inter-annual variation - specifically drought, these mean canopy temperatures were further increased or decreased by deviations from mean maximum air temperatures at 40 m pulled each day from the Tapajos eddy covariance tower1–3 and soil moisture at 40 cm depth (m3 m-3) which controlled canopy temperatures following equation 2 (Fig S6). Eq 2 – Canopy temperature (°C) = 46.5-33.6*soil moisture (m3 m-3) For example, in a non-drought year, on a day when max air temperatures were 0.1 °C higher than average and soil moisture was 0.01 m3 m-3 lower than average (which would add 0.3 °C to canopy temperatures (Eq 2)), we would add 0.4 °C to the grid canopy temperature that day. Every year, there was a 10% random probability of either a minor (80% probability) drought which reduced soil moisture by 0.1 m3 m-3 and increased air temperatures by 0.5 °C or severe drought (20% probability), which reduced soil moisture by 0.2 m3 m-3 and increased air temperatures by 1 °C. This is similar to the Amazon-wide temperature increases during the last El Niño 15. If an individual leaf temperature increases to above 46.7 °C (Tcrit) the leaf died, following Slot et al. (2021). Prior research has suggested that irreversible damage could begin at 45 °C 16 and T50 for tropical species is 49.9 °C 17, and we use these values in a sensitivity study. We further explore the impact of duration of Tcrit on mortality in a sensitivity study (ranging between needing a single exposure to four exposures to Tcrit to die). Over the season, if a leaf died, then it did not contribute towards canopy evapotranspiration. We ran simulations as a 3D canopy with an LAI of 5 where if the top leaf died, then it was replaced by a shade-adapted leaf with a Tcrit 1 °C lower 18. If each of the 5 LAIs died, then all leaves in that grid cell were dead and canopy evaporative cooling decreased by that percentage. Several lines of evidence suggest that under normal hydraulic conditions, when radiation load increases from ~350 to 1100 W m-2 (e.g. between shady and sunny conditions) average canopy temperature increases by ~3 °C and therefore, evaporative cooling for a full 1100 W m-2 is ~4.4°C4,19 (we vary this in a sensitivity study between 3.7 and 5.1°C). For example, if, over a year, 1000 leaves (10% of all leaves) surpass Tcrit and die, evaporative cooling for all leaves in the grid will be reduced by 10% (1000/(100 by 100 grid)) or 0.44 °C and 0.44 °C will be added to mean canopy temperature. Therefore, mean canopy temperature could heat up by a maximum of 4.4°C either due to a reduction of soil moisture or from an increase in dead leaves. We ran each simulation until the point where all leaves were dead and repeated this 30 times. We assumed loss of tree function following the death of all leaves, but we discuss this further in the discussion. We then ran sensitivity studies for several of the key variables (bold indicates the standard model parameter) including: drought (0.05, 0.1, to 0.2 m3 m-3 decrease in soil moisture), change in Tcrit (Tcrit: 45, 46.7, 49.9 °C), Tcrit range (100 by 100 grid =random distribution of 46.7±2, 100 by 100 grid =46.7±0), Max evaporative cooling (3.7, 4.4°C), (Tcrit duration (exceed Tcrit once, exceed Tcrit more than 3 times) and soil moisture coefficient (-33.6 -38.2; i.e. change the slope from Fig S6 by ± 1 sd). Methods References Miller, S. D. et al. Biometric and micrometeorological measurements of tropical forest carbon balance. Ecol. Appl. 14, 114–126 (2004). da Rocha, H. R. et al. Seasonality of water and heat fluxes over a tropical forest in eastern Amazonia. Ecol. Appl. 14, 22–32 (2004). Goulden, M. L. et al. Diel and seasonal patterns of tropical forest co2 exchange. Ecol. Appl. 14, 42–54 (2004). Kivalov, S. N. & Fitzjarrald, D. R. Observing the Whole-Canopy Short-Term Dynamic Response to Natural Step Changes in Incident Light: Characteristics of Tropical and Temperate Forests. Boundary-Layer Meteorol. 173, 1–52 (2019). Jin, M. & Liang, S. An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations. J. Clim. 19, (2006). Miller, S. D. et al. Reduced impact logging minimally alters tropical rainforest carbon and energy exchange. Proc. Natl. Acad. Sci. 108, 19431 LP – 19435 (2011). Doughty, C. E. An In Situ Leaf and Branch Warming Experiment in the Amazon. Biotropica 43, 658–665 (2011). Carter, K. R., Wood, T. E., Reed, S. C., Butts, K. M. & Cavaleri, M. A. Experimental warming across a tropical forest canopy height gradient reveals minimal photosynthetic and respiratory acclimation. Plant. Cell Environ. 44, 2879–2897 (2021). Rey-Sanchez, A. C., Slot, M., Posada, J. & Kitajima, K. Spatial and seasonal variation of leaf temperature within the canopy of a tropical forest. Clim. Res. 71, 75–89 (2016). Fauset, S. et al. Differences in leaf thermoregulation and water use strategies between three co-occurring Atlantic forest tree species. Plant. Cell Environ. 41, 1618–1631 (2018). Crous K Y, A W Cheesman, K Middleby, Rogers Eie, A Wujeska-Klause, A Y M Bouet, D S Ellsworth, M J Liddell, L A Cernusak, C V M Barton, Similar patterns of leaf temperatures and thermal acclimation to warming in temperate and tropical tree canopies., Tree Physiology, 2023;, tpad054, https://doi.org/10.1093/treephys/tpad054. Xiao, J., Fisher, J. B., Hashimoto, H., Ichii, K. & Parazoo, N. C. Emerging satellite observations for diurnal cycling of ecosystem processes. Nat. Plants 7, 877–887 (2021). Hulley, G. C. et al. Validation and Quality Assessment of the ECOSTRESS Level-2 Land Surface Temperature and Emissivity Product. IEEE Trans. Geosci. Remote Sens. 60, 1–23 (2022). Reichle, R., Lannoy, G. De, Koster, R. D., Crow, W. T. & 2017., J. S. K. SMAP L4 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 3. Boulder, Color. USA. NASA Natl. Snow Ice Data Cent. Distrib. Act. Arch. Center. doi https//doi.org/10.5067/B59DT1D5UMB4. (2017). Jiménez-Muñoz, J. C. et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 6, 33130 (2016). Berry, J. & Bjorkman, O. Photosynthetic Response and Adaptation to Temperature in Higher Plants. Annu. Rev. Plant Physiol. 31, 491–543 (1980). Slot, M. et al. Leaf heat tolerance of 147 tropical forest species varies with elevation and leaf functional traits, but not with phylogeny. Plant. Cell Environ. 44, (2021). Slot, M., Krause, G. H., Krause, B., Hernández, G. G. & Winter, K. Photosynthetic heat tolerance of shade and sun leaves of three tropical tree species. Photosynth. Res. 141, 119–130 (2019). Doughty, C. E. & Goulden, M. L. Are tropical forests near a high temperature threshold? J. Geophys. Res. Biogeosciences (2009) doi:10.1029/2007JG000632. The critical temperature beyond which photosynthetic machinery in tropical trees begins to fail averages ~46.7°C (Tcrit) 1. However, it remains unclear whether leaf temperatures experienced by tropical vegetation approach this threshold or soon will under climate change. We found that pantropical canopy temperatures independently triangulated from individual leaf thermocouples, pyrgeometers, and remote sensing (ECOSTRESS) have midday-peak temperatures of ~34°C during dry periods, with a long high-temperature tail that can exceed 40°C. Leaf thermocouple data from multiple sites across the tropics suggest that even within pixels of moderate temperatures, upper-canopy leaves exceed Tcrit 0.01% of the time. Further, upper-canopy leaf warming experiments (+2, 3, and 4°C in Brazil, Puerto Rico, and Australia) increased leaf temperatures non-linearly with peak leaf temperatures exceeding Tcrit 1.3% of the time (11% >43.5°C, 0.3% >49.9°C). Using an empirical model incorporating these dynamics (validated with warming experiment data), we found that tropical forests can withstand up to a 3.9 ± 0.5 °C increase in air temperatures before a potential collapse in metabolic function, but the remaining uncertainty in our understanding of Tcrit could reduce this to 2.6 ± 0.6°C. The 4.0°C estimate is within the “worst case scenario” (RCP-8.5) of climate change predictions2 for tropical forests and therefore it is still within our power to decide (e.g., by not taking the RCP 8.5 route) the fate of these critical realms of carbon, water, and biodiversity 3,4.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 24 Jul 2023Publisher:Dryad Authors:Doughty, Christopher;
Doughty, Christopher
Doughty, Christopher in OpenAIREGaillard, Camille;
Burns, Patrick; Keany, Jenna; +7 AuthorsGaillard, Camille
Gaillard, Camille in OpenAIREDoughty, Christopher;
Doughty, Christopher
Doughty, Christopher in OpenAIREGaillard, Camille;
Burns, Patrick; Keany, Jenna; Abraham, Andrew; Malhi, Yadvinder S.; Aguirre-Gutierrez, Jesus; Koch, George;Gaillard, Camille
Gaillard, Camille in OpenAIREJantz, Patrick;
Shenkin, Alexander; Tang, Hao;Jantz, Patrick
Jantz, Patrick in OpenAIREThe stratified nature of tropical forest structure had been noted by early explorers, but until recent use of satellite-based LiDAR (GEDI, or Global Ecosystems Dynamics Investigation LiDAR), it was not possible to quantify stratification across all tropical forests. Understanding stratification is important because by some estimates, a majority of the world’s species inhabit tropical forest canopies. Stratification can modify vertical microenvironment, and thus can affect a species’ susceptibility to anthropogenic climate change. Here we find that, based on analyzing each GEDI 25m diameter footprint in tropical forests (after screening for human impact), most footprints (60-90%) do not have multiple layers of vegetation. The most common forest structure has a minimum plant area index (PAI) at ~40m followed by an increase in PAI until ~15m followed by a decline in PAI to the ground layer (described hereafter as a one peak footprint). There are large geographic patterns to forest structure within the Amazon basin (ranging between 60–90% one peak) and between the Amazon (79 ± 9 % sd) and SE Asia or Africa (72 ± 14 % v 73 ±11 %). The number of canopy layers is significantly correlated with tree height (r2=0.12) and forest biomass (r2=0.14). Environmental variables such as maximum temperature (Tmax) (r2=0.05), vapor pressure deficit (VPD) (r2=0.03) and soil fertility proxies (e.g. total cation exchange capacity - r2=0.01) were also statistically significant but less strongly correlated given the complex and heterogeneous local structural to regional climatic interactions. Certain boundaries, like the Pebas Formation and Ecoregions, clearly delineate continental scale structural changes. More broadly, deviation from more ideal conditions (e.g. lower fertility or higher temperatures) leads to shorter, less stratified forests with lower biomass.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 24 Jul 2023Publisher:Dryad Authors:Doughty, Christopher;
Doughty, Christopher
Doughty, Christopher in OpenAIREGaillard, Camille;
Burns, Patrick; Keany, Jenna; +7 AuthorsGaillard, Camille
Gaillard, Camille in OpenAIREDoughty, Christopher;
Doughty, Christopher
Doughty, Christopher in OpenAIREGaillard, Camille;
Burns, Patrick; Keany, Jenna; Abraham, Andrew; Malhi, Yadvinder S.; Aguirre-Gutierrez, Jesus; Koch, George;Gaillard, Camille
Gaillard, Camille in OpenAIREJantz, Patrick;
Shenkin, Alexander; Tang, Hao;Jantz, Patrick
Jantz, Patrick in OpenAIREThe stratified nature of tropical forest structure had been noted by early explorers, but until recent use of satellite-based LiDAR (GEDI, or Global Ecosystems Dynamics Investigation LiDAR), it was not possible to quantify stratification across all tropical forests. Understanding stratification is important because by some estimates, a majority of the world’s species inhabit tropical forest canopies. Stratification can modify vertical microenvironment, and thus can affect a species’ susceptibility to anthropogenic climate change. Here we find that, based on analyzing each GEDI 25m diameter footprint in tropical forests (after screening for human impact), most footprints (60-90%) do not have multiple layers of vegetation. The most common forest structure has a minimum plant area index (PAI) at ~40m followed by an increase in PAI until ~15m followed by a decline in PAI to the ground layer (described hereafter as a one peak footprint). There are large geographic patterns to forest structure within the Amazon basin (ranging between 60–90% one peak) and between the Amazon (79 ± 9 % sd) and SE Asia or Africa (72 ± 14 % v 73 ±11 %). The number of canopy layers is significantly correlated with tree height (r2=0.12) and forest biomass (r2=0.14). Environmental variables such as maximum temperature (Tmax) (r2=0.05), vapor pressure deficit (VPD) (r2=0.03) and soil fertility proxies (e.g. total cation exchange capacity - r2=0.01) were also statistically significant but less strongly correlated given the complex and heterogeneous local structural to regional climatic interactions. Certain boundaries, like the Pebas Formation and Ecoregions, clearly delineate continental scale structural changes. More broadly, deviation from more ideal conditions (e.g. lower fertility or higher temperatures) leads to shorter, less stratified forests with lower biomass.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 16 Jun 2023Publisher:Dryad Authors:Aoyama, Lina;
Shaw, Elizabeth; White, Caitlin; Suding, Katharine; +1 AuthorsAoyama, Lina
Aoyama, Lina in OpenAIREAoyama, Lina;
Shaw, Elizabeth; White, Caitlin; Suding, Katharine; Hallett, Lauren;Aoyama, Lina
Aoyama, Lina in OpenAIREThis study was conducted at the University of California Sierra Foothills Research and Extension Center (SFREC), which is located north of Sacramento in Browns Valley, California (39º15’ N, 121º17’ W). Experimental Design In October 2014, we set up an experiment that manipulated the quantity and timing of rain and the plant community composition (Hallett et al., 2019; Shaw et al., 2022). We did not need permits for fieldwork. Water year 2015 (October 2014 to May 2015) was the final year of a 6-year drought, among the worst on state record since record-keeping began in 1895 in California (California Department of Water Resources 2017). In a random-block design, plant community composition treatments were nested within rainfall treatment plots in 4 blocks (Fig. S1). Rainfall treatments consisted of control (ambient rainfall), consistent dry (50% of rain blocked from October–May), fall dry (50% of rain blocked from October–January), and spring dry (50% of rain blocked from February–May). A 50% rain reduction represents roughly a one-in-ten-year drought. Rainfall treatments were effective in their respective windows (e.g., fall dry and consistently dry lowered volumetric soil moisture in the fall), although duration of the drought effect in fall was shorter compared to that in spring due to a late start to the season (Fig. S2). Within rainfall treatments, three 1 x 2 m community composition subplots were established for a total of 48 subplots: 4 rainfall plots x 3 composition subplots x 4 blocks. Community composition treatments were two single functional group treatments (only annual grasses or only forbs) and a mixture of both functional groups. Prior to seeding the composition treatments, we removed litter and applied post-emergence herbicide during a sunny period when seedlings were around 1 inch tall. We used Poast herbicide (BASF Ag Products) to remove grass seedlings in the only forb plots, and 2,4D herbicide (Dow Chemical) to remove forb seedlings in the only annual grass plots. No herbicide was applied in the mixed plots. We followed the herbicide application with hand weeding of legumes in all three composition treatments. We seeded 4 g/m2 of Erodium botrys in the forb-only plots, 4 g/m2 of Bromus hordeaceus, Lolium multiflorum, and Avena barbata in the grass-only plots, and nothing in the mixed plots. We seeded these species because they are the most dominant forb and grass species at the field site. We did not think the difference in number of species sown would disproportionately increase functional trait diversity in the grass plots, because forbs from the seedbank emerged after seeding, and functional diversity in this system is largely influenced by forb abundance (Hallett et al. 2017). Because E. botrys densities were variable across blocks, we transplanted individuals into the plots to reach a density of at least 10 individuals/m2 in the forb and mixture plots. Apart from E. botrys transplants, the mixed functional group treatment was simply what emerged from the existing seed bank. Species composition and biomass Following one growing season, peak species composition and biomass were collected in May 2015. A 1 m2 quadrat was laid out within each subplot, and all plants present in the quadrat were identified to species and visual estimates of their percent cover were recorded. Additionally, a visual estimate of the percent cover of grass, forb, bare ground, and litter cover was also recorded. Aboveground net primary productivity (ANPP) was harvested by clipping plant biomass down to the soil surface from a 0.25 m x 0.25 m quadrat. Fresh biomass was placed in a drying oven at 60°C for 48 h. Samples were weighed after drying. Belowground net primary productivity (BNPP) was harvested by separating roots from a 5 cm diameter x 30 cm deep soil core in the same location as the ANPP clipping. Briefly, the core was divided into three 10 cm segments and roots were picked out of each segment with forceps in 10-minute intervals, for a total of 40 minutes per segment (Metcalfe et al., 2007). Roots were gently washed with tap water over a 2 mm sieve to remove any soil particles (Fisher Scientific No. 10), dried in a 60 °C oven for 48 h, and then weighed. ANPP and BNPP data are presented as grams of dry biomass per m2. Plant traits For 16 out of 37 species present at our site, we used a trait database available from Butterfield & Suding (2013). We replicated their methods to collect traits on the remaining 21 species, with 5 species not included because they were rare members of the community or did not germinate (Table S1). Specifically, we collected plant traits from individuals grown in a greenhouse for one season (6 weeks after germination). We used the mean trait value of six individuals as the trait value for each species. The following aboveground traits were measured: plant height, specific leaf area (SLA), and leaf dry matter content (LDMC). Height was measured from the tip of the newest tiller to the bottom of the oldest tiller using a ruler. One leaf (second newest, mature leaf) per individual was cut, scanned, and weighed fresh for fresh leaf area and weight. Then, these leaves were dried in a 60 °C oven for 48 h and weighed to obtain dry leaf weight. Resource-acquisitive species are generally taller and have larger and fleshier leaves (i.e., high SLA; low LDMC) than resource-conservative species. These traits are consistent predictors of aboveground biomass (e.g., Butterfield & Suding, 2013; Cheng et al., 2021; Finegan et al., 2015). The following belowground traits were measured: root tissue density, specific root length of coarse (> 2 mm diameter) and fine roots (≤ 2 mm diameter) separately, coarse root diameter, and proportion of fine roots. Roots were washed with tap water over 2 mm sieve, stored in 50% ethanol in a 4°C refrigerator, then scanned and analyzed using WinRhizo (Regent Instruments, Siante-Foy, Quebec, Canada) to measure belowground traits. Resource-acquisitive species have finer roots with low root tissue density and high specific root length compared to resource-conservative species (Reich, 2014; Tjoelker et al., 2005; Weemstra et al., 2016). Specific root length is a trait that has been related to the root’s efficiency to water and nutrient acquisition, since it indicates the amount of root length achieved per unit root mass invested (Lambers et al., 2006; Ostonen et al., 2007). Root tissue density has been linked to drought tolerance in arid environments (Butterfield et al., 2017). Understanding precipitation controls on functional diversity is important in predicting how change in rainfall patterns will alter plant productivity in the future. Trait-based approaches can provide predictive knowledge about how certain species will behave and interact with the community. However, how functional diversity relates to above- and belowground biomass production in variable rainfall conditions remains unclear. Here, we tested the role of mass ratio and niche complementarity hypotheses in shaping above- and belowground biomass-functional diversity relationships in seasonal drought. We implemented a fully crossed experiment that manipulated drought timing (fall dry, spring dry, consistent dry, and ambient rainfall) and community composition (grass-dominated, forb-dominated, and mixed grass-forb) in a California annual grassland. Plant communities with mixed functional groups showed higher above- and belowground biomass than either the grass- or forb-dominant communities. We found divergent functional diversity-biomass relationships for above- and belowground biomass. Aboveground biomass decreased with community-weighted means (CWMs) of SLA and height, supporting the mass ratio hypothesis, which posits that dominant species with specific traits drive biomass production of the community. Belowground biomass showed no evidence of either mass ratio hypothesis or niche complementarity. While biomass was largely unaffected by the timing of drought in one season, we found community-wide functional trait shifts in response to rainfall treatments. Aboveground traits shifted to higher SLA in consistent dry compared to ambient. Belowground traits shifted to longer, finer and denser roots in fall and consistently dry, and shorter and coarser roots in spring dry. Functional diversity buffered biomass production by enabling shifts in above- and belowground functional traits across variable rainfall conditions.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Top 10% influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 16 Jun 2023Publisher:Dryad Authors:Aoyama, Lina;
Shaw, Elizabeth; White, Caitlin; Suding, Katharine; +1 AuthorsAoyama, Lina
Aoyama, Lina in OpenAIREAoyama, Lina;
Shaw, Elizabeth; White, Caitlin; Suding, Katharine; Hallett, Lauren;Aoyama, Lina
Aoyama, Lina in OpenAIREThis study was conducted at the University of California Sierra Foothills Research and Extension Center (SFREC), which is located north of Sacramento in Browns Valley, California (39º15’ N, 121º17’ W). Experimental Design In October 2014, we set up an experiment that manipulated the quantity and timing of rain and the plant community composition (Hallett et al., 2019; Shaw et al., 2022). We did not need permits for fieldwork. Water year 2015 (October 2014 to May 2015) was the final year of a 6-year drought, among the worst on state record since record-keeping began in 1895 in California (California Department of Water Resources 2017). In a random-block design, plant community composition treatments were nested within rainfall treatment plots in 4 blocks (Fig. S1). Rainfall treatments consisted of control (ambient rainfall), consistent dry (50% of rain blocked from October–May), fall dry (50% of rain blocked from October–January), and spring dry (50% of rain blocked from February–May). A 50% rain reduction represents roughly a one-in-ten-year drought. Rainfall treatments were effective in their respective windows (e.g., fall dry and consistently dry lowered volumetric soil moisture in the fall), although duration of the drought effect in fall was shorter compared to that in spring due to a late start to the season (Fig. S2). Within rainfall treatments, three 1 x 2 m community composition subplots were established for a total of 48 subplots: 4 rainfall plots x 3 composition subplots x 4 blocks. Community composition treatments were two single functional group treatments (only annual grasses or only forbs) and a mixture of both functional groups. Prior to seeding the composition treatments, we removed litter and applied post-emergence herbicide during a sunny period when seedlings were around 1 inch tall. We used Poast herbicide (BASF Ag Products) to remove grass seedlings in the only forb plots, and 2,4D herbicide (Dow Chemical) to remove forb seedlings in the only annual grass plots. No herbicide was applied in the mixed plots. We followed the herbicide application with hand weeding of legumes in all three composition treatments. We seeded 4 g/m2 of Erodium botrys in the forb-only plots, 4 g/m2 of Bromus hordeaceus, Lolium multiflorum, and Avena barbata in the grass-only plots, and nothing in the mixed plots. We seeded these species because they are the most dominant forb and grass species at the field site. We did not think the difference in number of species sown would disproportionately increase functional trait diversity in the grass plots, because forbs from the seedbank emerged after seeding, and functional diversity in this system is largely influenced by forb abundance (Hallett et al. 2017). Because E. botrys densities were variable across blocks, we transplanted individuals into the plots to reach a density of at least 10 individuals/m2 in the forb and mixture plots. Apart from E. botrys transplants, the mixed functional group treatment was simply what emerged from the existing seed bank. Species composition and biomass Following one growing season, peak species composition and biomass were collected in May 2015. A 1 m2 quadrat was laid out within each subplot, and all plants present in the quadrat were identified to species and visual estimates of their percent cover were recorded. Additionally, a visual estimate of the percent cover of grass, forb, bare ground, and litter cover was also recorded. Aboveground net primary productivity (ANPP) was harvested by clipping plant biomass down to the soil surface from a 0.25 m x 0.25 m quadrat. Fresh biomass was placed in a drying oven at 60°C for 48 h. Samples were weighed after drying. Belowground net primary productivity (BNPP) was harvested by separating roots from a 5 cm diameter x 30 cm deep soil core in the same location as the ANPP clipping. Briefly, the core was divided into three 10 cm segments and roots were picked out of each segment with forceps in 10-minute intervals, for a total of 40 minutes per segment (Metcalfe et al., 2007). Roots were gently washed with tap water over a 2 mm sieve to remove any soil particles (Fisher Scientific No. 10), dried in a 60 °C oven for 48 h, and then weighed. ANPP and BNPP data are presented as grams of dry biomass per m2. Plant traits For 16 out of 37 species present at our site, we used a trait database available from Butterfield & Suding (2013). We replicated their methods to collect traits on the remaining 21 species, with 5 species not included because they were rare members of the community or did not germinate (Table S1). Specifically, we collected plant traits from individuals grown in a greenhouse for one season (6 weeks after germination). We used the mean trait value of six individuals as the trait value for each species. The following aboveground traits were measured: plant height, specific leaf area (SLA), and leaf dry matter content (LDMC). Height was measured from the tip of the newest tiller to the bottom of the oldest tiller using a ruler. One leaf (second newest, mature leaf) per individual was cut, scanned, and weighed fresh for fresh leaf area and weight. Then, these leaves were dried in a 60 °C oven for 48 h and weighed to obtain dry leaf weight. Resource-acquisitive species are generally taller and have larger and fleshier leaves (i.e., high SLA; low LDMC) than resource-conservative species. These traits are consistent predictors of aboveground biomass (e.g., Butterfield & Suding, 2013; Cheng et al., 2021; Finegan et al., 2015). The following belowground traits were measured: root tissue density, specific root length of coarse (> 2 mm diameter) and fine roots (≤ 2 mm diameter) separately, coarse root diameter, and proportion of fine roots. Roots were washed with tap water over 2 mm sieve, stored in 50% ethanol in a 4°C refrigerator, then scanned and analyzed using WinRhizo (Regent Instruments, Siante-Foy, Quebec, Canada) to measure belowground traits. Resource-acquisitive species have finer roots with low root tissue density and high specific root length compared to resource-conservative species (Reich, 2014; Tjoelker et al., 2005; Weemstra et al., 2016). Specific root length is a trait that has been related to the root’s efficiency to water and nutrient acquisition, since it indicates the amount of root length achieved per unit root mass invested (Lambers et al., 2006; Ostonen et al., 2007). Root tissue density has been linked to drought tolerance in arid environments (Butterfield et al., 2017). Understanding precipitation controls on functional diversity is important in predicting how change in rainfall patterns will alter plant productivity in the future. Trait-based approaches can provide predictive knowledge about how certain species will behave and interact with the community. However, how functional diversity relates to above- and belowground biomass production in variable rainfall conditions remains unclear. Here, we tested the role of mass ratio and niche complementarity hypotheses in shaping above- and belowground biomass-functional diversity relationships in seasonal drought. We implemented a fully crossed experiment that manipulated drought timing (fall dry, spring dry, consistent dry, and ambient rainfall) and community composition (grass-dominated, forb-dominated, and mixed grass-forb) in a California annual grassland. Plant communities with mixed functional groups showed higher above- and belowground biomass than either the grass- or forb-dominant communities. We found divergent functional diversity-biomass relationships for above- and belowground biomass. Aboveground biomass decreased with community-weighted means (CWMs) of SLA and height, supporting the mass ratio hypothesis, which posits that dominant species with specific traits drive biomass production of the community. Belowground biomass showed no evidence of either mass ratio hypothesis or niche complementarity. While biomass was largely unaffected by the timing of drought in one season, we found community-wide functional trait shifts in response to rainfall treatments. Aboveground traits shifted to higher SLA in consistent dry compared to ambient. Belowground traits shifted to longer, finer and denser roots in fall and consistently dry, and shorter and coarser roots in spring dry. Functional diversity buffered biomass production by enabling shifts in above- and belowground functional traits across variable rainfall conditions.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Top 10% influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Embargo end date: 24 Aug 2020Publisher:Dryad Photosynthetic heat tolerances (PHTs) have several potential applications including predicting which species will be most vulnerable to climate change. Given that plants exhibit unique thermoregulatory traits that influence leaf temperatures and decouple them from ambient air temperatures, we hypothesized that PHTs should be correlated with extreme leaf temperatures as opposed to air temperatures. We measured leaf thermoregulatory traits, maximum leaf temperatures (TMO) and two metrics of PHTs (Tcrit and T50) quantified using the quantum yfield of photosystem II for 19 plant species growing in Fairchild Tropical Botanic Garden (Coral Gables, FL, USA). Thermoregulatory traits measured at the Garden and microenvironmental variables were used to parameterize a leaf energy balance model that estimated maximum in situ leaf temperatures (TMIS) across the geographic distributions of 13 species. T MO and TMIS were positively correlated with T50 but were not correlated with Tcrit. The breadth of species' thermal safety margins (the difference between T50 and TMO) was negatively correlated with T50. Our results provide observational and theoretical support based on a first principles approach for the hypothesis that PHTs may be adaptations to extreme leaf temperature, but refute the assumption that species with higher PHTs are less susceptible to thermal damage. Our study also introduces a novel method for studying plant ecophysiology by incorporating biophysical and species distribution models, and highlights how the use of air temperature versus leaf temperature can lead to conflicting conclusions about species vulnerability to thermal damage.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 3visibility views 3 download downloads 1 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Embargo end date: 24 Aug 2020Publisher:Dryad Photosynthetic heat tolerances (PHTs) have several potential applications including predicting which species will be most vulnerable to climate change. Given that plants exhibit unique thermoregulatory traits that influence leaf temperatures and decouple them from ambient air temperatures, we hypothesized that PHTs should be correlated with extreme leaf temperatures as opposed to air temperatures. We measured leaf thermoregulatory traits, maximum leaf temperatures (TMO) and two metrics of PHTs (Tcrit and T50) quantified using the quantum yfield of photosystem II for 19 plant species growing in Fairchild Tropical Botanic Garden (Coral Gables, FL, USA). Thermoregulatory traits measured at the Garden and microenvironmental variables were used to parameterize a leaf energy balance model that estimated maximum in situ leaf temperatures (TMIS) across the geographic distributions of 13 species. T MO and TMIS were positively correlated with T50 but were not correlated with Tcrit. The breadth of species' thermal safety margins (the difference between T50 and TMO) was negatively correlated with T50. Our results provide observational and theoretical support based on a first principles approach for the hypothesis that PHTs may be adaptations to extreme leaf temperature, but refute the assumption that species with higher PHTs are less susceptible to thermal damage. Our study also introduces a novel method for studying plant ecophysiology by incorporating biophysical and species distribution models, and highlights how the use of air temperature versus leaf temperature can lead to conflicting conclusions about species vulnerability to thermal damage.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Embargo end date: 23 Feb 2024Publisher:Dryad # **Developmental temperature, more than long-term evolution, defines thermal tolerance in an estuarine copepod** DOI: 10.5061/dryad.wm37pvmvb This dataset contains five data files. The first data file contains results from an experimental evolution study in the copepod, *Acartia tonsa*. In this study we examined the effect of ocean warming on the upper lethal temperature of *A. tonsa*. The second file contains results from the same study, where we instead looked at the impact of ocean warming on lower lethal salinity. In the third file, we examined the impact of rapid salinity change on upper lethal temperature in the copepod *A. tonsa*. The last two files contain supplementary data that were used for supplemental figures. These files contain data from preliminary experiments where upper lethal temperature and lower lethal salinity were tested. 1\) UCONN_ULT_Final 2\) UCONN_LLS_Reps_NoTreat 3\) SalinityTemp 4\) LHS_ctmax_Complete 5\) UCONN_GradualSalinity_Trial **Description of the data and file structure** In **UCONN_ULT_Final** there are six variables: Line, Replicate, Treatment, Group, Condition, and ULT. These variables are defined below. * Line: Categorical variable with two levels, Control and HighTemp. Control line animals were from UConn lines held at 18C and HighTemp line animals were from UConn lines held at 22C. * Replicate: This categorical variable has four levels referring to which replicate line animals originated from. * Treatment: Categorical variable with two levels Ambient and OceanWarming. Animals at Ambient were held at 18C at the time of the upper lethal temperature assay. Animals at OceanWarming were held at 22C at the time of the upper lethal temperature assay. * Group: Six level categorical variable defining each unique combination of line and treatment. * Condition: This variable is redundant to Group, with a different naming scheme for analysis in R. * ULT: Continuous variable, upper lethal temperature values for individual copepods. In **UCONN_LLS_Reps_NoTreat** there are six variables: Line, Replicate, Group, Condition, Treatment, and OLLS. These variables are defined below. * Line: Categorical variable with two levels, Ambient and HighTemp. Ambient line animals were from UConn lines held at 18C and HighTemp line animals were from UConn lines held at 22C. * Replicate: This categorical variable has four levels referring to which replicate line animals originated from. * Treatment: Categorical variable with two levels 18 and 22. Animals at 18 were held at 18C at the time of the upper lethal temperature assay. Animals at 22 were held at 22C at the time of the upper lethal temperature assay. * Group: Six level categorical variable defining each unique combination of line and treatment. * Condition: This variable is redundant to Group, with a different naming scheme for analysis in R. * OLLS: Observed lower lethal salinity for each individual copepod. In **SalinityTemp** there are three variables: Population, Salinity, and ULT. * Population: Categorical variable with two levels: HB, and UCONNControl. HB represents animals collected in Hammonasset Beach State Park, CT. UCONNControl represents animals from the UConn originating ambient line animals held at 18C. * Salinity: This is a categorical variable with three levels. Each level represents the salinity treatment copepods were exposed to in ppt. * ULT: This is a continuous variable representing the upper lethal temperature of each individual copepod. **LHS_ctmax_Complete** is a dataset with six variables: Population, Plate, Stage, Ctmax, Sex, and MeanMax. * Population: Categorical variable with three levels: Maine, NewYork, and Florida. * Plate: Categorical variable with three levels, designating which six well plate animals were in. * Stage: Categorical variable with three levels: Adult, Copepodite, and Nauplii. These are the life history stages copepods were at when they were assessed for their upper lethal temperature. * Ctmax: This is a continuous variable with upper lethal temperature values for each individual copepod. * Sex: Categorical variable with three levels: Male, Female, and Unknown. * MeanMax: Mean maximum temperature collected from buoys near each population site. In **UCONN_GradualSalinity_Trial** there are five variables: Line, Condition, Replicate, Sex, and LLS. * Line: Categorical variable with one level, Control. These represent animals that originated from UConn control line held at 18C. * Condition: This is a categorical variable with one level, High. This represents that the animals were held at 22C during the salinity trial. * Replicate: This is a categorical variable with one level, 2. This represents the replicate line the animals originated from. * Sex: Categorical variable with two levels: Male and Female. This represents the sex of the individual copepod in the salinity trial. * LLS: This is a continuous variable for the lower lethal salinity of each individual copepod. **Code/Software** Code can be found at this Github repository: Climate change is resulting in increasing ocean temperatures and salinity variability, particularly in estuarine environments. Tolerance of temperature and salinity change interact and thus may impact organismal resilience. Populations can respond to multiple stressors in the short-term (i.e., plasticity) or over longer timescales (i.e., adaptation). However, little is known about the short- or long-term effects of elevated temperature on the tolerance of acute temperature and salinity changes. Here we characterized the response of the near-shore and estuarine copepod, Acartia tonsa, to temperature and salinity stress. Copepods originated from one of two sets of replicated >40 generation-old temperature adapted lines: Ambient (AM, 18°C) and ocean warming (OW, 22°C). Copepods from these lines were subjected to one and three generations at the reciprocal temperature. Copepods from all treatments were then assessed for differences in acute temperature and salinity tolerance. Development (one generation), three generations, and >40 generations of warming increased thermal tolerance compared to Ambient conditions, with development in OW resulting in equal thermal tolerance to three and >40 generations of OW. Strikingly, developmental OW and >40 generations of OW had no effect on low salinity tolerance relative to Ambient. By contrast, when environmental salinity was reduced first, copepods had lower thermal tolerances. These results highlight a critical role for plasticity in the copepod climate response and suggest that salinity variability may reduce copepod tolerance to subsequent warming.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Embargo end date: 23 Feb 2024Publisher:Dryad # **Developmental temperature, more than long-term evolution, defines thermal tolerance in an estuarine copepod** DOI: 10.5061/dryad.wm37pvmvb This dataset contains five data files. The first data file contains results from an experimental evolution study in the copepod, *Acartia tonsa*. In this study we examined the effect of ocean warming on the upper lethal temperature of *A. tonsa*. The second file contains results from the same study, where we instead looked at the impact of ocean warming on lower lethal salinity. In the third file, we examined the impact of rapid salinity change on upper lethal temperature in the copepod *A. tonsa*. The last two files contain supplementary data that were used for supplemental figures. These files contain data from preliminary experiments where upper lethal temperature and lower lethal salinity were tested. 1\) UCONN_ULT_Final 2\) UCONN_LLS_Reps_NoTreat 3\) SalinityTemp 4\) LHS_ctmax_Complete 5\) UCONN_GradualSalinity_Trial **Description of the data and file structure** In **UCONN_ULT_Final** there are six variables: Line, Replicate, Treatment, Group, Condition, and ULT. These variables are defined below. * Line: Categorical variable with two levels, Control and HighTemp. Control line animals were from UConn lines held at 18C and HighTemp line animals were from UConn lines held at 22C. * Replicate: This categorical variable has four levels referring to which replicate line animals originated from. * Treatment: Categorical variable with two levels Ambient and OceanWarming. Animals at Ambient were held at 18C at the time of the upper lethal temperature assay. Animals at OceanWarming were held at 22C at the time of the upper lethal temperature assay. * Group: Six level categorical variable defining each unique combination of line and treatment. * Condition: This variable is redundant to Group, with a different naming scheme for analysis in R. * ULT: Continuous variable, upper lethal temperature values for individual copepods. In **UCONN_LLS_Reps_NoTreat** there are six variables: Line, Replicate, Group, Condition, Treatment, and OLLS. These variables are defined below. * Line: Categorical variable with two levels, Ambient and HighTemp. Ambient line animals were from UConn lines held at 18C and HighTemp line animals were from UConn lines held at 22C. * Replicate: This categorical variable has four levels referring to which replicate line animals originated from. * Treatment: Categorical variable with two levels 18 and 22. Animals at 18 were held at 18C at the time of the upper lethal temperature assay. Animals at 22 were held at 22C at the time of the upper lethal temperature assay. * Group: Six level categorical variable defining each unique combination of line and treatment. * Condition: This variable is redundant to Group, with a different naming scheme for analysis in R. * OLLS: Observed lower lethal salinity for each individual copepod. In **SalinityTemp** there are three variables: Population, Salinity, and ULT. * Population: Categorical variable with two levels: HB, and UCONNControl. HB represents animals collected in Hammonasset Beach State Park, CT. UCONNControl represents animals from the UConn originating ambient line animals held at 18C. * Salinity: This is a categorical variable with three levels. Each level represents the salinity treatment copepods were exposed to in ppt. * ULT: This is a continuous variable representing the upper lethal temperature of each individual copepod. **LHS_ctmax_Complete** is a dataset with six variables: Population, Plate, Stage, Ctmax, Sex, and MeanMax. * Population: Categorical variable with three levels: Maine, NewYork, and Florida. * Plate: Categorical variable with three levels, designating which six well plate animals were in. * Stage: Categorical variable with three levels: Adult, Copepodite, and Nauplii. These are the life history stages copepods were at when they were assessed for their upper lethal temperature. * Ctmax: This is a continuous variable with upper lethal temperature values for each individual copepod. * Sex: Categorical variable with three levels: Male, Female, and Unknown. * MeanMax: Mean maximum temperature collected from buoys near each population site. In **UCONN_GradualSalinity_Trial** there are five variables: Line, Condition, Replicate, Sex, and LLS. * Line: Categorical variable with one level, Control. These represent animals that originated from UConn control line held at 18C. * Condition: This is a categorical variable with one level, High. This represents that the animals were held at 22C during the salinity trial. * Replicate: This is a categorical variable with one level, 2. This represents the replicate line the animals originated from. * Sex: Categorical variable with two levels: Male and Female. This represents the sex of the individual copepod in the salinity trial. * LLS: This is a continuous variable for the lower lethal salinity of each individual copepod. **Code/Software** Code can be found at this Github repository: Climate change is resulting in increasing ocean temperatures and salinity variability, particularly in estuarine environments. Tolerance of temperature and salinity change interact and thus may impact organismal resilience. Populations can respond to multiple stressors in the short-term (i.e., plasticity) or over longer timescales (i.e., adaptation). However, little is known about the short- or long-term effects of elevated temperature on the tolerance of acute temperature and salinity changes. Here we characterized the response of the near-shore and estuarine copepod, Acartia tonsa, to temperature and salinity stress. Copepods originated from one of two sets of replicated >40 generation-old temperature adapted lines: Ambient (AM, 18°C) and ocean warming (OW, 22°C). Copepods from these lines were subjected to one and three generations at the reciprocal temperature. Copepods from all treatments were then assessed for differences in acute temperature and salinity tolerance. Development (one generation), three generations, and >40 generations of warming increased thermal tolerance compared to Ambient conditions, with development in OW resulting in equal thermal tolerance to three and >40 generations of OW. Strikingly, developmental OW and >40 generations of OW had no effect on low salinity tolerance relative to Ambient. By contrast, when environmental salinity was reduced first, copepods had lower thermal tolerances. These results highlight a critical role for plasticity in the copepod climate response and suggest that salinity variability may reduce copepod tolerance to subsequent warming.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 21 Aug 2023Publisher:Dryad Authors:Tourville, Jordon;
Publicover, David; Dovciak, Martin;Tourville, Jordon
Tourville, Jordon in OpenAIRERemote sensing analysis Physical copies of true color high resolution historical aerial imagery (sub-meter resolution) were acquired from the Appalachian Mountain Club (AMC) and the USFS White Mountain National Forest Headquarters. Imagery for the Presidential Range was taken in 1978 and Katahdin imagery was taken in 1991. Hard copy images were scanned and converted to TIFF format at 300 dpi (resulting in 0.5 m resolution images). Spatial analyses of change in treeline positions over time were enabled by acquiring high resolution 2018 false-color near-infrared imagery from the National Agriculture Inventory Program (NAIP 2021). Both sets of imagery were taken during summer months (1:40,000 scale). Using ArcGIS 10.8 (ESRI 2011, Redlands, CA, USA), historic imagery was ortho- and georectified to newer imagery via a spline function along 60 ground control points, and then converted into one orthomosaic image (RMSE < 1m). Exact error was always below 5 m for each individual image. All areas above treeline were manually digitized based on observed tree cover for both sets of images, and the resulting polygons were converted to raster format at 2 m resolution (all raster pixels within each polygon had a value of 1). We identified forest cover only as areas with overlapping crowns and seen as green reflectance in historic imagery and red reflectance in contemporary false-color near-infrared imagery (no visible bare earth or easily identified alpine vegetation). Isolated tree island edges were also digitized and included as treeline if they were >20 m in diameter in any direction (determined in ArcGIS) and included an individual >2 m in height as validated in the field. Alpine rasters were aligned to and multiplied by Lidar-derived digital elevation models (DEMs; 2 m resolution) acquired from New Hampshire and Maine state GIS repositories in order to determine treeline elevations. A total of 400 random sample points (200 for each range, using the ArcGIS random sample point tool) were placed along the outer boundary of the alpine rasters derived from our contemporary imagery, and for each of them we established a paired point at the nearest location along the alpine raster boundary derived from our historic imagery. Field surveys Field sampling was carried out in the summer of 2021 to characterize tree demography and demographic variation among different treeline forms identified from the current imagery. A subset of contemporary points from our GIS-based sample point pairs (n = 54, 33 in the Presidential Range, 21 in the Katahdin Range, see above) were selected using a random number generator to serve as sites for establishing belt transects. Each belt transect was 100 m in length and 4 m wide (2 m on either side of transect for a total area of 400 m2) and perpendicular to elevation contours, spanning the ecotone between closed forest interior and open alpine habitat. The start of each transect (the lowest elevation on the transect, set as 0 m) was located 50 m downslope (straight-line distance) of contemporary sample points. The start and end of each belt transect were recorded using a Garmin GPSMAP 64 (Garmin, Olathe, Kansas, USA). Each tree > 0.1 m in height with a stem rooted within the transect was recorded noting species, basal diameter (10 cm from the ground), height, horizontal distance from the transect, and distance along the transect (to estimate stem density of trees). Slope, aspect, elevation, and soil depth to bedrock (using a metal soil probe) were recorded at 20 m intervals along the belt transect centerline (0 m, 20 m, 40 m, 60 m, 80 m, 100 m). For all belt transects, treeline form was assigned based on visual assessments (based on changes in tree height and density across the ecotone). Additionally, we visited a majority of our other accessible contemporary random sample points (~80%) in order to assign treeline form and ground-truth remote sensed treeline classifications. For all visited sample points we took a new GPS point at the field-verified treeline location (continuous canopy cover and at least one individual >2 m in height) nearest to our random sample points (assigned from our treeline delineation procedure). The new points were compared to the original sample point locations and assessed for accuracy (measuring linear distance between points). Eye-level photos of treelines were taken at all sample points to keep a permanent record of treeline appearance. We stress that because tree height could not be extracted or field validated from our historic imagery, some krummholz individuals (<2 m) may have been present above our treeline delineation using our classification scheme. Out of all 400 sample point pairs across both the Presidentials and Katahdin, 88 were classified as abrupt (22%), 70 as diffuse (17.5%), 84 as island (21%), and 162 as krummholz (40.5%). Spatial data processing To examine the factors potentially influencing the spatial dynamics of treeline advance, both climatological and topographical variables were extracted for the Presidential Range. We could not conduct a similar analysis for Katahdin given the lack of fine-scale climatological data in that area. Elevation was extracted from 2 m state produced DEMs. Using the Spatial Analyst toolbox in ArcGIS, topographical variables such as slope, aspect, and curvature (measure of convex or concave shape of the terrain ranging between -4 and 4) were extracted from our DEMs. Circular aspect data (measured in degrees, 0-360⁰) were converted to radians and linearized (east and west = 1, north and south = 0). Before linearization, aspect values were used to calculate degree difference from prevailing wind (DDPW - 290˚) and degree difference from south (DDS - 180˚) variables. DDPW is a proxy for exposure to strong winds that can cause both direct physical damage and damage from icing, as well as a proxy for the potential for snow accumulation. The prevailing wind direction for the Presidential range (290˚) was based on wind measurements from the Mount Washington Observatory. DDS is a proxy for the amount of direct solar radiation (in the northern hemisphere). Average monthly mean, maximum, and minimum temperatures as well as annual accumulated growing degree days (AGDD) were calculated from an array of 34 HOBO dataloggers (Onset Computer Corporation, Bourne, MA, USA) placed at various elevations and adjacent to Appalachian Mountain Club buildings in the White Mountains of New Hampshire. HOBO loggers have recorded hourly air temperature at ground level (0 m height) continuously since 2007. Air temperature means and AGDD were calculated from HOBO logger data; for AGDD calculations we used a base temperature of 4˚C, consistent with other studies examining growth patterns of balsam fir, the dominant species within studied treelines. AGDD was calculated as the accumulated maximum value of growing degree days (GDD) in a year. Gridded maps (90 m spatial resolution) of mean annual temperature (Tmean, between 2007 and 2020) and AGDD for the Presidential Range region were produced using a cokriging interpolation method. To do this, temperatures and AGDD response variables were first checked for normality using qq-plots. Next, correlation between response variables and potential covariates was assessed; both elevation and aspect were highly correlated with HOBO derived temperature and AGDD. We used normal-score simple cokriging with a stable semi-variogram model to interpolate (prediction map) climate variables over the entire spatial extent of the Presidential Range (RMSE ~ 1 for both Tmean and AGDD). Mean annual precipitation was estimated from 30-year normal PRISM climate data (1991-2020; PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu). Aim Alpine treeline ecotones are influenced by environmental drivers and are anticipated to shift their locations in response to changing climate. Our goal was to determine the extent of recent climate-induced treeline advance in the northeastern United States, and we hypothesized that treelines have advanced upslope in complex ways depending on treeline structure and environmental conditions. Location White Mountain National Forest (New Hampshire) and Baxter State Park (Maine), USA. Taxon High-elevation trees – Abies balsamea, Picea mariana, and Betula cordata. Methods We compared current and historical high-resolution aerial imagery to quantify the advance of treelines over the last four decades, and link treeline changes to treeline form (demography) and environmental drivers. Spatial analyses were coupled with ground surveys of forest vegetation and topographical features to ground-truth treeline classification and provide information on treeline demography and additional potential drivers of treeline locations. We used multiple linear regression models to examine the importance of both topographic and climatic variables on treeline advance. Results Regional treelines have significantly shifted upslope over the past several decades (on average by 3 m/decade). Diffuse treelines (low tree densities and temperature limited) experienced significantly greater upslope shifts (5 m/decade) compared to other treeline forms, suggesting that both climate warming and treeline demography are important drivers of treeline shifts. Topographical features (slope, aspect) as well as climate (accumulated growing degree days, AGDD) explained significant variation in the magnitude of treeline advance (R2 = 0.32). Main conclusions The observed advance of regional treelines suggests that climate warming induces upslope treeline shifts particularly at higher elevations where greater upslope shifts occurred in areas with lower AGDD. Overall, our findings suggest that diffuse treelines at high-elevations are more a of a result of climate warming than other alpine treeline ecotones and thus they can serve as key indicators of ongoing climatic changes. Associated csv's require R (or Excel) to be loaded and for data to be analyzed.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 6visibility views 6 download downloads 4 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Embargo end date: 21 Aug 2023Publisher:Dryad Authors:Tourville, Jordon;
Publicover, David; Dovciak, Martin;Tourville, Jordon
Tourville, Jordon in OpenAIRERemote sensing analysis Physical copies of true color high resolution historical aerial imagery (sub-meter resolution) were acquired from the Appalachian Mountain Club (AMC) and the USFS White Mountain National Forest Headquarters. Imagery for the Presidential Range was taken in 1978 and Katahdin imagery was taken in 1991. Hard copy images were scanned and converted to TIFF format at 300 dpi (resulting in 0.5 m resolution images). Spatial analyses of change in treeline positions over time were enabled by acquiring high resolution 2018 false-color near-infrared imagery from the National Agriculture Inventory Program (NAIP 2021). Both sets of imagery were taken during summer months (1:40,000 scale). Using ArcGIS 10.8 (ESRI 2011, Redlands, CA, USA), historic imagery was ortho- and georectified to newer imagery via a spline function along 60 ground control points, and then converted into one orthomosaic image (RMSE < 1m). Exact error was always below 5 m for each individual image. All areas above treeline were manually digitized based on observed tree cover for both sets of images, and the resulting polygons were converted to raster format at 2 m resolution (all raster pixels within each polygon had a value of 1). We identified forest cover only as areas with overlapping crowns and seen as green reflectance in historic imagery and red reflectance in contemporary false-color near-infrared imagery (no visible bare earth or easily identified alpine vegetation). Isolated tree island edges were also digitized and included as treeline if they were >20 m in diameter in any direction (determined in ArcGIS) and included an individual >2 m in height as validated in the field. Alpine rasters were aligned to and multiplied by Lidar-derived digital elevation models (DEMs; 2 m resolution) acquired from New Hampshire and Maine state GIS repositories in order to determine treeline elevations. A total of 400 random sample points (200 for each range, using the ArcGIS random sample point tool) were placed along the outer boundary of the alpine rasters derived from our contemporary imagery, and for each of them we established a paired point at the nearest location along the alpine raster boundary derived from our historic imagery. Field surveys Field sampling was carried out in the summer of 2021 to characterize tree demography and demographic variation among different treeline forms identified from the current imagery. A subset of contemporary points from our GIS-based sample point pairs (n = 54, 33 in the Presidential Range, 21 in the Katahdin Range, see above) were selected using a random number generator to serve as sites for establishing belt transects. Each belt transect was 100 m in length and 4 m wide (2 m on either side of transect for a total area of 400 m2) and perpendicular to elevation contours, spanning the ecotone between closed forest interior and open alpine habitat. The start of each transect (the lowest elevation on the transect, set as 0 m) was located 50 m downslope (straight-line distance) of contemporary sample points. The start and end of each belt transect were recorded using a Garmin GPSMAP 64 (Garmin, Olathe, Kansas, USA). Each tree > 0.1 m in height with a stem rooted within the transect was recorded noting species, basal diameter (10 cm from the ground), height, horizontal distance from the transect, and distance along the transect (to estimate stem density of trees). Slope, aspect, elevation, and soil depth to bedrock (using a metal soil probe) were recorded at 20 m intervals along the belt transect centerline (0 m, 20 m, 40 m, 60 m, 80 m, 100 m). For all belt transects, treeline form was assigned based on visual assessments (based on changes in tree height and density across the ecotone). Additionally, we visited a majority of our other accessible contemporary random sample points (~80%) in order to assign treeline form and ground-truth remote sensed treeline classifications. For all visited sample points we took a new GPS point at the field-verified treeline location (continuous canopy cover and at least one individual >2 m in height) nearest to our random sample points (assigned from our treeline delineation procedure). The new points were compared to the original sample point locations and assessed for accuracy (measuring linear distance between points). Eye-level photos of treelines were taken at all sample points to keep a permanent record of treeline appearance. We stress that because tree height could not be extracted or field validated from our historic imagery, some krummholz individuals (<2 m) may have been present above our treeline delineation using our classification scheme. Out of all 400 sample point pairs across both the Presidentials and Katahdin, 88 were classified as abrupt (22%), 70 as diffuse (17.5%), 84 as island (21%), and 162 as krummholz (40.5%). Spatial data processing To examine the factors potentially influencing the spatial dynamics of treeline advance, both climatological and topographical variables were extracted for the Presidential Range. We could not conduct a similar analysis for Katahdin given the lack of fine-scale climatological data in that area. Elevation was extracted from 2 m state produced DEMs. Using the Spatial Analyst toolbox in ArcGIS, topographical variables such as slope, aspect, and curvature (measure of convex or concave shape of the terrain ranging between -4 and 4) were extracted from our DEMs. Circular aspect data (measured in degrees, 0-360⁰) were converted to radians and linearized (east and west = 1, north and south = 0). Before linearization, aspect values were used to calculate degree difference from prevailing wind (DDPW - 290˚) and degree difference from south (DDS - 180˚) variables. DDPW is a proxy for exposure to strong winds that can cause both direct physical damage and damage from icing, as well as a proxy for the potential for snow accumulation. The prevailing wind direction for the Presidential range (290˚) was based on wind measurements from the Mount Washington Observatory. DDS is a proxy for the amount of direct solar radiation (in the northern hemisphere). Average monthly mean, maximum, and minimum temperatures as well as annual accumulated growing degree days (AGDD) were calculated from an array of 34 HOBO dataloggers (Onset Computer Corporation, Bourne, MA, USA) placed at various elevations and adjacent to Appalachian Mountain Club buildings in the White Mountains of New Hampshire. HOBO loggers have recorded hourly air temperature at ground level (0 m height) continuously since 2007. Air temperature means and AGDD were calculated from HOBO logger data; for AGDD calculations we used a base temperature of 4˚C, consistent with other studies examining growth patterns of balsam fir, the dominant species within studied treelines. AGDD was calculated as the accumulated maximum value of growing degree days (GDD) in a year. Gridded maps (90 m spatial resolution) of mean annual temperature (Tmean, between 2007 and 2020) and AGDD for the Presidential Range region were produced using a cokriging interpolation method. To do this, temperatures and AGDD response variables were first checked for normality using qq-plots. Next, correlation between response variables and potential covariates was assessed; both elevation and aspect were highly correlated with HOBO derived temperature and AGDD. We used normal-score simple cokriging with a stable semi-variogram model to interpolate (prediction map) climate variables over the entire spatial extent of the Presidential Range (RMSE ~ 1 for both Tmean and AGDD). Mean annual precipitation was estimated from 30-year normal PRISM climate data (1991-2020; PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu). Aim Alpine treeline ecotones are influenced by environmental drivers and are anticipated to shift their locations in response to changing climate. Our goal was to determine the extent of recent climate-induced treeline advance in the northeastern United States, and we hypothesized that treelines have advanced upslope in complex ways depending on treeline structure and environmental conditions. Location White Mountain National Forest (New Hampshire) and Baxter State Park (Maine), USA. Taxon High-elevation trees – Abies balsamea, Picea mariana, and Betula cordata. Methods We compared current and historical high-resolution aerial imagery to quantify the advance of treelines over the last four decades, and link treeline changes to treeline form (demography) and environmental drivers. Spatial analyses were coupled with ground surveys of forest vegetation and topographical features to ground-truth treeline classification and provide information on treeline demography and additional potential drivers of treeline locations. We used multiple linear regression models to examine the importance of both topographic and climatic variables on treeline advance. Results Regional treelines have significantly shifted upslope over the past several decades (on average by 3 m/decade). Diffuse treelines (low tree densities and temperature limited) experienced significantly greater upslope shifts (5 m/decade) compared to other treeline forms, suggesting that both climate warming and treeline demography are important drivers of treeline shifts. Topographical features (slope, aspect) as well as climate (accumulated growing degree days, AGDD) explained significant variation in the magnitude of treeline advance (R2 = 0.32). Main conclusions The observed advance of regional treelines suggests that climate warming induces upslope treeline shifts particularly at higher elevations where greater upslope shifts occurred in areas with lower AGDD. Overall, our findings suggest that diffuse treelines at high-elevations are more a of a result of climate warming than other alpine treeline ecotones and thus they can serve as key indicators of ongoing climatic changes. Associated csv's require R (or Excel) to be loaded and for data to be analyzed.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 6visibility views 6 download downloads 4 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.ncjsxkszw&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu