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  • Authors: orcid bw Tang, Wenxi;
    Tang, Wenxi
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Tang, Wenxi in OpenAIRE
    Liu, Shuguang; Jing, Mengdan; Healey, John; +5 Authors

    # Vegetation growth responses to climate change: a cross-scale analysis of biological memory and time-lags using tree ring and satellite data The dataset includes tree-ring data for individual trees across three species, encompassing dimensionless tree-ring width (TRW) measurements, as well as data on the enhanced vegetation index (EVI), leaf area index (LAI), gross primary productivity (GPP), and various climate parameters. The TRW serves as an indicator of radial stem growth at the tree-species level. Remote sensing-based data of EVI, LAI and GPP were used to monitor ecosystem-scale canopy dynamics, leaf growth, and ecosystem carbon sequestration capacity, respectively. ## Description of the data and file structure 1. Climate_1956_2017.csv: The dataset includes the mean air temperature, mean maximum air temperature, mean minimum air temperature, mean sunshine duration, and total precipitation from 1956 to 2017 on a daily basis in the study area. *Notes*: Lat, Latitude; Lon, longitude; Elev, Elevation; MTEM, mean air temperature (ºC); MaxTEM, mean maximum air temperature (ºC); MinTEM, mean maximum air temperature (ºC); X20to20PRE, accumulated precipitation at 20-20 (mm); SSD, mean sunshine duration (h). 2. TRW_LF.csv: This dataset comprises data for each core of individual trees belonging to the Liquidambar formosana (LF), coded as LF_01A, where 'LF' denotes the tree species, '01' represents the tree number, and 'A' indicates the core sample number taken from each tree. The units for this tree-ring data are in 0.001mm. 3. TRW_CE.csv: This dataset comprises data for each core of individual trees belonging to the Castanopsis eyrei (CE), coded as CE_01A, where 'CE' denotes the tree species, '01' represents the tree number, and 'A' indicates the core sample number taken from each tree. The units for this tree-ring data are in 0.001mm. 4. TRW_CH.csv: This dataset comprises data for each core of individual trees belonging to the Castanea henryi (CH), coded as CH_01A, where 'CH' denotes the tree species, '01' represents the tree number, and 'A' indicates the core sample number taken from each tree. The units for this tree-ring data are in 0.001mm. 5. Dimensionless_TRW_data_of_the_three_tree_species.csv: Between October 2020 and July 2022, we sampled 25-29 mature and healthy trees per species, collecting one-to-two cores from each tree at 1.3 m above the ground using a 5.15 mm increment borer. The tree-ring cores were fixed, dried, polished, and visually cross-dated under a binocular microscope. We measured tree-ring width with the LINTAB™ 6 system to a 0.01-mm accuracy, covering data from 1957 to 2017. Standardization of tree-ring width data involved two phases. First, COFECHA software ensured the quality of cross-dating results by evaluating the synchronization of growth patterns across samples. Next, we used the detrend function from the dplR package in R to fit a modified negative exponential curve to each raw tree-ring series for detrending. Standardized indices were calculated by dividing the original ring widths by the fitted values and combining them into a single standardized chronology using a bi-weight robust mean to mitigate outlier influence. *Notes*: CE, Castanopsis eyrei; CH, Castanea henryi; LF, Liquidambar formosana. 6. EVI_MOD13Q1_16days.csv: The dataset consists of the enhanced vegetation index (EVI) for the study area, measured over 16-day periods. *Notes*: Start, date of start; End, date of start; EVI, enhanced vegetation index (unitless). 7. LAI_MCD15A2H_16days.csv: The dataset consists of the leaf area index (LAI) for the study area, measured over 16-day periods. To ensure a consistent time resolution for remote sensing-based vegetation indicators, the 8-day time periods of LAI was aligned with the 16-day time periods of EVI. This alignment was achieved by averaging LAI values from two consecutive 8-day periods. *Notes*: Start, date of start; End, date of start; LAI, leaf area index (m2/m2). 8. GPP_MOD17A2H_16days.csv: The dataset consists of the gross primary productivity (GPP) for the study area, measured over 16-day periods. To ensure a consistent time resolution for remote sensing-based vegetation indicators, the 8-day time periods of GPP was aligned with the 16-day time periods of EVI. This alignment was achieved by calculating GPP as the cumulative value of two consecutive 8-day periods. *Notes*: Start, date of start; End, date of start; GPP, gross primary productivity (kg C/m2). Vegetation growth is affected by past growth rates and climate variability. However, the impacts of vegetation growth carryover (VGC; biotic) and lagged climatic effects (LCE; abiotic) on tree stem radial growth may be decoupled from photosynthetic capacity, as higher photosynthesis does not always translate into greater growth. To assess the interaction of tree-species level VGC and LCE with ecosystem-scale photosynthetic processes, we utilized tree-ring width (TRW) data for three tree species: Castanopsis eyrei (CE), Castanea henryi (CH, Chinese chinquapin), and Liquidambar formosana (LF, Chinese sweet gum), along with satellite-based data on canopy greenness (EVI, enhanced vegetation index), leaf area index (LAI), and gross primary productivity (GPP). We used vector autoregressive models, impulse response functions, and forecast error variance decomposition to analyze the duration, intensity, and drivers of VGC and of LCE response to precipitation, temperature, and sunshine duration. The results showed that at the tree-species level, VGC in TRW was strongest in the first year, with an average 77% reduction in response intensity by the fourth year. VGC and LCE exhibited species-specific patterns; compared to CE and CH (diffuse-porous species), LF (ring-porous species) exhibited stronger VGC but weaker LCE. For photosynthetic capacity at the ecosystem scale (EVI, LAI, and GPP), VGC and LCE occurred within 96 days. Our study demonstrates that VGC effects play a dominant role in vegetation function and productivity, and that vegetation responses to previous growth states are decoupled from climatic variability. Additionally, we discovered the possibility for tree-ring growth to be decoupled from canopy condition. Investigating VGC and LCE of multiple indicators of vegetation growth at multiple scales has the potential to improve the accuracy of terrestrial global change models. The dataset includes tree-ring data for individual trees across three species, encompassing dimensionless tree-ring width (TRW) measurements, as well as data on the enhanced vegetation index (EVI), leaf area index (LAI), gross primary productivity (GPP), and various climate parameters. The TRW serves as an indicator of radial stem growth at the tree-species level. Remote sensing-based data of EVI, LAI and GPP were used to monitor ecosystem-scale canopy dynamics, leaf growth, and ecosystem carbon sequestration capacity, respectively. Dimensionless tree-ring width (TRW) measurements method: Between October 2020 and July 2022, we sampled 25-29 mature and healthy trees per species, collecting one-to-two cores from each tree at 1.3 m above the ground using a 5.15 mm increment borer. The tree-ring cores were fixed, dried, polished, and visually cross-dated under a binocular microscope. We measured tree-ring width with the LINTAB™ 6 system to a 0.01-mm accuracy, covering data from 1957 to 2017. Standardization of tree-ring width data involved two phases. First, COFECHA software ensured the quality of cross-dating results by evaluating the synchronization of growth patterns across samples. Next, we used the detrend function from the dplR package in R to fit a modified negative exponential curve to each raw tree-ring series for detrending. Standardized indices were calculated by dividing the original ring widths by the fitted values and combining them into a single standardized chronology using a bi-weight robust mean to mitigate outlier influence.

    DRYADarrow_drop_down
    DRYAD
    Dataset . 2024
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    Data sources: Datacite
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      DRYAD
      Dataset . 2024
      License: CC 0
      Data sources: Datacite
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    Authors: orcid bw Doughty, Christopher;
    Doughty, Christopher
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Doughty, Christopher in OpenAIRE
    orcid bw Gaillard, Camille;
    Gaillard, Camille
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Gaillard, Camille in OpenAIRE
    Burns, Patrick; Keany, Jenna; +7 Authors

    The 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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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Kreitmair, Monika; Draper, Scott; Borthwick, Alistair; van den Bremer, Ton;

    Uncertainty affects estimates of the power potential of tidal currents, resulting in large ranges in values reported for a given site, such as the Pentland Firth, UK. We examine the role of bottom friction, one of the most important sources of uncertainty. We do so by using perturbation methods to find the leading-order effect of bottom friction uncertainty in theoretical models by Garrett & Cummins (2005), Vennell (2010), and Garrett & Cummins (2013), which consider quasi-steady flow in a channel completely spanned by tidal turbines, a similar channel but retaining the inertial term, and a circular turbine farm in laterally unconfined flow. We find that bottom friction uncertainty acts to increase estimates of expected power in a fully-spanned channel, but generally has the reverse effect in laterally unconfined farms. The optimal number of turbines, accounting for bottom friction uncertainty, is lower for a fully-spanned channel and higher in laterally unconfined farms. We estimate the typical magnitude of bottom friction uncertainty, which suggests that the effect on estimates of expected power lies in the range −5 to +30%, but is probably small for deep channels such as the Pentland Firth (5-10%). In such a channel, the uncertainty in power estimates due to bottom friction uncertainty remains considerable, and we estimate a relative standard derivation of 30%, increasing to 50% for small channels. V10_optimum_RSOSMatlab code used to find the value of turbine drag parameter which maximises the expected power under uncertain bed roughness parameter for the model presented in R.A. Vennell, Tuning Tidal Turbines In-Concert to Maximise Farm Efficiency, J. Fluid Mech., 671:587–604, 2010.

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    ZENODO
    Dataset . 2018
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2018
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2018
    Data sources: B2FIND
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    EASY
    Dataset . 2018
    Data sources: EASY
    DRYAD
    Dataset . 2018
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2018
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2018
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2018
      Data sources: B2FIND
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      EASY
      Dataset . 2018
      Data sources: EASY
      DRYAD
      Dataset . 2018
      License: CC 0
      Data sources: Datacite
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    Authors: Price, Elliott L.; Perić, Mirela Sertić; Gustavo, Romero Q.; Kratina, Pavel;

    1. The changes to physical and chemical ecosystem characteristics as a response to pervasive and intensifying land use have the potential to alter the consumer-resource interactions and to rewire the flow of energy through entire food webs. 2. We investigated these structural and functional properties of food webs in stream ecosystems distributed across woodland, agricultural and urban areas in the Zagreb region of Croatia. We compared resource availability and consumer diet composition using stable isotope mixing models and tested how the isotopic variance of basal resources, primary consumers, macroinvertebrate predators, and other food-web characteristics change with different land use types. 3. Combination of increased loading and altered composition of nutrients, lower water discharge and higher light availability at urban sites likely promoted the contribution of aquatic macrophytes to diets of primary consumers. Macroinvertebrate predators shifted their diet, relying more on active filterers at urban sites relative to woodland and agricultural sites. Urban food webs also had lower trophic redundancy (i.e. fewer species at each trophic level) and a more homogenised energy flow from lower to higher trophic levels. There was no effect of land use on isotopic variation of basal resources, primary consumers or macroinvertebrate predators, but all these trophic groups at urban and agricultural sites were 15N-enriched relative to their counterparts in woodland stream food webs. 4. The physical and chemical ecosystem characteristics associated with intensive land use altered the resource availability, trophic redundancy and the flow of energy to other trophic levels, with potentially negative consequences for community dynamics and ecosystem functioning. These empirical findings indicate that reducing nutrient pollution, agricultural runoffs and maintaining riparian vegetation can mitigate the impacts of land use on structure and function of stream ecosystems. INVERTSExcel file showing bulk delta13C and delta15N isotope values from macroinvertebrates collected in freshwater steams in the region of Zagreb, Croatia in April to May of 2016. Other columns include taxonomic and functional information about each sample that was analysed for isotopes, site name, stream name, and the land use classification for each sampling station.SOURCESExcel file showing bulk delta13C and delta15N isotope values from food source materials collected in freshwater steams in the region of Zagreb, Croatia in April to May of 2016. Other columns include the type of resource, the classification of the resource in terms of allocthonous or autochthonous, and the site name, stream name, and the land use classification for each sampling station.

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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2019
    Data sources: B2FIND
    DRYAD
    Dataset . 2019
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2019
      Data sources: B2FIND
      DRYAD
      Dataset . 2019
      License: CC 0
      Data sources: Datacite
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  • Authors: orcid bw Pérez, Giovanny;
    Pérez, Giovanny
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Pérez, Giovanny in OpenAIRE

    # Data from: Avian phylogenetic and functional diversity are better conserved by land-sparing than land-sharing farming in lowland tropical forests [https://doi.org/10.5061/dryad.n5tb2rc40](https://doi.org/10.5061/dryad.n5tb2rc40) Files provided include raw (field) and pre-processed datasets (CSV and RDS files), and R software scripts (4 files) to conduct all analyses described in the Journal article. ## Description of the data and file structure We provide the minimum amount of data necessary to reproduce all the code. Scripts are numbered and should be run sequentially as each script relies on outputs generated in the previous ones. ### Attached files. ED_values_amazonbirds.csv * species: Scientific name following taxonomy from Jetz et al., (2012) * ED_mean: Evolutionary distinctness computed using the fair proportions method (Kembel et al., 2010) and averaged across 10000 phylogenetic trees. * EDR_mean: ED divided by species range area from Tobias et al., (2022) Functional_traits_amazonbirds.csv * species: Scientific name following taxonomy from Jetz et al., (2012) * Beak_Length_Culmen: Length from the tip of the beak to the base of the skull (mm) * Beak_Length_Nares: Length from the anterior edge of the nostrils to the tip of the beak (mm) * Beak_Width: Width of the beak at the anterior edge of the nostrils (mm) * Beak_Depth: Depth of the beak at the anterior edge of the nostrils (mm) * Tarsus_Length: Length of the tarsus from the posterior notch to the acrotarsium (mm) * Wing_Length: Length from the carpal joint to the tip of the longest primary (mm) * Kipps_Distance: Length from the tip of the first secondary feather to the tip of the longest primary (mm) * Secondary1: Length from the carpal joint to the tip of the first secondary (mm) * Hand_Wing_Index: 100*Kipp’s distance / Wing length * Tail_Length: Distance between the longest rectrix tip and central rectrices protrusion (mm) * logMass: Body mass given as species average (g, log scaled) * LogClutch_Size: Average number of eggs per clutch (log scaled) * Gen_Length: Average age of parents (log scaled) * Trophic_Level: Categorical variable. Herbivore: species obtaining at least 70% of food resources from plants; Carnivore: species obtaining at least 70% of food resources by consuming live invertebrate or vertebrate animals; Scavenger: species obtaining at least 70% of food resources from carrion or refuse; Omnivore: species obtaining resources from multiple trophic level in roughly equal proportion. * Trophic_Niche: Categorical variable. Frugivore: species obtaining at least 60% of food resources from fruit; Granivore: species obtaining at least 60% of food resources from seeds or nuts; Nectarivore: species obtaining at least 60% of food resources from nectar; Herbivore: species obtaining at least 60% of food resources from other plant materials in non-aquatic systems; Herbivore aquatic: species obtaining at least 60% of food resources from plant materials in aquatic systems; Invertivore: species obtaining at least 60% of food resources from invertebrates in terrestrial systems; Vertivore: species obtaining at least 60% of food resources from vertebrate animals in terrestrial systems; Aquatic Predator: species obtaining at least 60% of food resources from vertebrate and invertebrate animals in aquatic systems; Scavenger: species obtaining at least 60% of food resources from carrion, offal or refuse; Omnivore: Species using multiple niches, within or across trophic levels, in relatively equal proportions * Primary_Lifestyle: Categorical variable. Aerial: species spends much of the time in flight, and hunts or forages predominantly on the wing; Terrestrial: species spends majority of its time on the ground, where it obtains food while either walking or hopping; Insessorial: species spends much of the time perching above the ground, either in branches of trees and other vegetation (i.e. arboreal), or on other raised substrates including rocks, buildings, posts, and wires; Aquatic = species spends much of the time sitting on water, and obtains food while afloat or when diving under the water's surface; Generalist = species has no primary lifestyle because it spends time in different lifestyle classes. * Nest_Placement: Categorical variable. Extent to which a species depends of forest structures for nest placement. Ground open nesting; elevated open nesting; cavity nesting. names_lookup_amazonbirds.csv * model: Currently valid scientific name * species: Scientific name following taxonomy from Jetz et al., (2012) dataset_raw_amazonbirds.csv * point: Sampling point identifier. * species: Currently valid scientific name. * v1: Detections (1), non-detections (0) in visit 1. * v2: Detections (1), non-detections (0) in visit 2. * v3: Detections (1), non-detections (0) in visit 3. * v4: Detections (1), non-detections (0) in visit 4. * Q: Presence (1), Absence (0) of species across visits. * lat: Geographic coordinates (WGS84) latitude. * lon: Geographic coordinates (WGS84) longitude. * site: Study site identifiers; PS: Amazonas, PL: Putumayo, SG: Guaviare. * cluster: sampling cluster identifier. * habitat: Categorical, sampling point habitat; forest or pasture. * habitat2: habitat in numerical form; -1: pasture, 1: forest * elev_ALOS: Sampling point elevation in meters above sea level. * forest_dependency: Species dependency to forest habitat according to birdlife.org; High, medium, low * obs1: Identifier of researcher conducting sampling during visit 1. * obs2: Identifier of researcher conducting sampling during visit 2. * obs3: Identifier of researcher conducting sampling during visit 3. * obs4: Identifier of researcher conducting sampling during visit 4. * prop: Proportion of wildlife-friendly features in sampling point * prop_sc: Proportion of wildlife-friendly features in sampling point, standardised and centred. * hps1_sc: Time of day in scientific notation, standardised and centred during visit 1. * hps2_sc: Time of day in scientific notation, standardised and centred during visit 2. * hps3_sc: Time of day in scientific notation, standardised and centred during visit 3. * hps4_sc: Time of day in scientific notation, standardised and centred during visit 4. * site_sp: Species and study site interaction effect identifier. phylogeny_amazonbirds.rds * Trimmed phylogenetic tress (10000) to observed species, stored as an RDS object to be open in R software. 0_Modelling_and_simulation.R * R programming language script to 1) model occupancy of amazon birds in forest and pasture habitats and 2) Predict avian communities in habitats and simulated agricultural landscapes. 1_PD_FD_habitats.R * R programming language script to compute Phylogenetic and Functional Diversity metrics across forest and pastures habitats across a gradient of wildlife-friendly features. 2_PD_FD_sharing_sparing.R * R programming language script to compute Phylogenetic and Functional Diversity metrics across land-sharing and land-sparing simulated agriultural landscapes. 3_PD_FD_figures.R * R programming language script to replicate plots, figures and results from analyses in the journal article. ## Sharing/Access information Links to other publicly accessible locations of the data: * [https://github.com/gapz01/PhD_year_one](https://github.com/gapz01/PhD_year_one) Data was derived from the following sources: * vertlife.org: [https://birdtree.org/downloads/](https://birdtree.org/downloads/) * [https://figshare.com/s/b990722d72a26b5bfead](https://figshare.com/s/b990722d72a26b5bfead) ## Code/Software The following packages must be installed in R for scripts to run (R version used 4.0.2) flocker, brms, tidyverse, DataCombine, ape, PhyloMeasures, mFD, fundiversity, picante, viridis, patchwork, ggpubr, tidybayes, bayestestR. The transformation of natural habitats for farming is a major driver of tropical biodiversity loss. To mitigate impacts, two alternatives are promoted: intensifying agriculture to offset protected areas (land sparing) or integrating wildlife-friendly habitats within farmland (land sharing). In the montane and dry tropics, phylogenetic and functional diversity, which underpin evolutionary values and the provision of ecosystem functioning and services, are best protected by land sparing. A key question is how these components of biodiversity are best conserved in the more stable environments of lowland moist tropical forests. Focusing on cattle farming within the Colombian Amazon, we sampled how the occupancy of 280 bird species varies between forest and pasture spanning gradients of wildlife-friendly features. We then simulated scenarios of land-sparing and land-sharing farming to predict impacts on phylogenetic and functional diversity metrics. Predicted metrics differed marginally between forest and pasture. However, community assembly varied significantly. Wildlife-friendly pastures were inadequate for most forest-dependent species, while phylogenetic and functional diversity indices showed minimal variation across gradients of wildlife-friendly features. Land sparing consistently retained higher levels of Faith’s phylogenetic diversity (~30%), functional richness (~20%), and evolutionarily distinct lineages (~40%) than land sharing, and did so across a range of landscape sizes. Securing forest protection through land-sparing practices remains superior for conserving overall community phylogenetic and functional diversity than land sharing. Synthesis and applications: To minimise the loss of avian phylogenetic diversity and functional traits from farming in the Amazon, it is imperative to protect large blocks of undisturbed and regenerating forests. The intensification required within existing farmlands to make space for spared lands whilst meeting agricultural demand needs to be sustainable, avoiding long-term negative impacts on soil quality and other ecosystem services. Policies need to secure the delivery of both actions simultaneously.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: orcid bw Pelle, Tyler;
    Pelle, Tyler
    ORCID
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    Pelle, Tyler in OpenAIRE
    Greenbaum, Jamin; Dow, Christine; Jenkins, Adrian; +1 Authors

    # Data from: Subglacial discharge accelerates future retreat of Denman and Scott Glaciers, East Antarctica [https://doi.org/10.7280/D1X12S](https://doi.org/10.7280/D1X12S) Journal: Science Advances Principle Investigator: * Tyler Pelle, Scripps Institution of Oceanography, University of California San Diego, [tpelle@ucsd.edu](mailto:tpelle@ucsd.edu) Co-Authors: * Dr. Jamin Greenbaum, Scripps Institution of Oceanography, University of California San Diego, [jsgreenbaum@ucsd.edu](mailto:jsgreenbaum@ucsd.edu) * Dr. Christine Dow, Department of Geography and Environmental Management, University of Waterloo, [christine.dow@uwaterloo.ca](mailto:christine.dow@uwaterloo.ca) * Dr. Adrian Jenkins, Department of Geography and Environmental Sciences, Northumbria University, [adrian2.jenkins@northumbria.ac.uk](mailto:adrian2.jenkins@northumbria.ac.uk) * Dr. Mathieu Morlighem, Department of Earth Sciences, Dartmouth College, [Mathieu.Morlighem@dartmouth.edu](mailto:Mathieu.Morlighem@dartmouth.edu) Created on September 5, 2023 ## Description of the data and file structure ### File description: 1. runme.m - MATLAB script used to compute melt rates with and without considering subglacial discharge. 2. ice_ctrl_sd.mat – Yearly ice sheet model output from 2017-2100 for the control subglacial discharge simulation. 3. ice_ctrl_nosd.mat – Yearly ice sheet model output from 2017-2100 for the control non-subglacial discharge simulation. 4. ice_ssp126_sd.mat – Yearly ice sheet model output from 2017-2100 for the low emission (SSP1-2.6) subglacial discharge simulation. 5. ice_ssp126_nosd.mat – Yearly ice sheet model output from 2017-2100 for the low emission (SSP1-2.6) non-subglacial discharge simulation. 6. ice_ssp585_sd.mat – Yearly ice sheet model output from 2017-2100 for the high emission (SSP5-8.5) subglacial discharge simulation. 7. ice_ssp585_nosd.mat – Yearly ice sheet model output from 2017-2100 for the high emission (SSP5-8.5) non-subglacial discharge simulation. 8. T_MITgcm_ctrl.mat – Bi-weekly ocean temperature (Ta) for the control simulation from January 1, 2017 to December 31, 2299 averaged at the ice shelf terminus of Denman and Scott Glaciers, used as input into the melt parameterization. 9. S_MITgcm_ctrl.mat – Bi-weekly ocean salinity (Sa) for the control simulation from January 1, 2017 to December 31, 2299 averaged at the ice shelf terminus of Denman and Scott Glaciers, used as input into the melt parameterization. 10. T_MITgcm126.mat - Same as T_MITgcm_ctrl.mat, but for the low emission scenario. 11. S_MITgcm126.mat - Same as S_MITgcm_ctrl.mat, but for the low emission scenario. 12. T_MITgcm585.mat - Same as T_MITgcm_ctrl.mat, but for the high emission scenario. 13. S_MITgcm585.mat - Same as S_MITgcm_ctrl.mat, but for the high emission scenario. 14. discharge_den.xyz - 2017 modeled channelized subglacial discharge flux output from GlaDS, used as input into the melt parameterization. 15. DenModel.mat – Ice sheet model initial state (January 1, 2021), including all mesh information, ice sheet and ice shelf geometry, inversion fields for basal friction and ice stiffness, and initial state variables. ### File specific information: **DenModel.mat**: All data associated with the ice sheet model initial state is held in DenModel.mat, which contains a MATLAB ‘model’ object (for more information, see [https://issm.jpl.nasa.gov/documentation/modelclass/](https://issm.jpl.nasa.gov/documentation/modelclass/)). In MATLAB, the model can be loaded and displayed by running load(‘DenModel.mat’), which will load in the model variable ‘md’. Of particular interest will be the following data contained in md: md.mesh (mesh information), md.geometry (initial ice sheet geometry, ice shelf geometry, and bed topography), and md.mask (ice mask and grounded ice mask). Note that all fields are defined on the mesh nodes, and one can plot a given field in MATLAB using the ISSM tool ‘plotmodel’. Once IceSheetModel.mat is loaded, we can plot the ice shelf basal melting rate by running the following command: plotmodel(md, ’data’, md.results.TransientSolution(1).BasalforcingsFloatingiceMeltingRate). For more information on plotting, please see [https://issm.jpl.nasa.gov/documentation/plotmatlab/](https://issm.jpl.nasa.gov/documentation/plotmatlab/). **ice_*_sd.mat**: Yearly ice sheet model results between 2017–2300 for the subglacial discharge experiments, where ‘*’ is either ctrl (control), ssp126 (low emission scenario), or ssp585 (high emission scenario). These files contain a MATLAB variable that is the same as the file name, which is a model object of size 1x283 that contains the following yearly variables: * Vel (velocity norm, m/yr) * Pressure (N/A since we use a 2D ice flow model) * Thickness (ice sheet thickness, m) * Surface (ice sheet surface elevation, m) * Base (ice sheet base elevation, m) * BasalforcingsFloatingiceMeltingRate (ice shelf basal melting rate field, m/yr) * MaskOceanLevelset (ground ice mask, grounded ice if > 0, grounding line position if = 0, floating ice if < 0) * IceVolume (total ice volume in the model domain, t) * IceVolumeAboveFloatation (total ice volume in the model domain that is above hydrostatic equilibrium, t) * TotalFloatingBmb (Total floating basal mass balance, Gt) * melt_nodis (ice shelf basal melting rate computed when discharge is set to zero, m/yr) * zgl (grounding line height field, m) * glfw (grounding line fresh water flux field, m2/s) * chan_wid (Domain average subglacial discharge channel width, m) * maxdist (5L' length scale used in melt computation, m) * maxdis (maximum discharge at each subglacial outflow location, m2/s) **ice_*_nosd.mat:** Same file information as ice_*_sd.mat, but for the non-subglacial discharge ice sheet model simulations without the following variables: melt_nodis, zgl, glfw, chan_wid, max_dist, and max_dis. **T_MITgcm*.mat**: Bi-weekly ocean temperature extracted from an East Antarctic configuration of the MITgcm from Pelle et al. (2021), where '*' is ctrl (control), ssp126 (low emission scenario), or ssp585 (high emission scenario). Ocean temperature was averaged at the ice front of Denman and Scott Glaciers (see contour in Figure 1 in the main text) at the lowest ocean level. Ocean temperature data is in units of degrees Celsius. **S_MITgcm*.mat**: Same as above, but for salinity in units on the Practical Salinity Scale (PSU). **discharge_den.xyz:** GlaDS output present-day channelized subglacial discharge flux (m3/s). To load the data and interpolate onto the model mesh, use: ``` [x,y,dis] = xyz2grid('discharge_den.xyz'); dis_den = InterpFromGridToMesh(x(1,:)',flipud(y(:,1)),flipud(dis),md.mesh.x,md.mesh.y,0); ``` where 'InterpFromGridToMesh' is a module built into ISSM. Ice sheet model results: Direct results taken from the Ice-sheet and Sea-level System Model (ISSM, Larour et al. 2012) with no processing applied, provided yearly as *.mat files. Ice sheet model initial state: Initial state (ice geometry, mesh information, inversion results, etc.) of the ice sheet model containing Denman and Scott Galciers with no processing applied, provided as a *.mat file. The contents of the *.mat file is a MATLAB variable of class "model", which is compatible with ISSM. Melt parameterization script: Documented MATLAB script ready to run with the provided data sets. Ocean temperature and salinity timeseries: Bottom ocean temperature (°C) and salinity (PSU) timeseries (January 1st, 2017 through December 31, 2299) extracted from an East Antarctic configuration of the ocean component of the MITgcm (Pelle et al., 2021). Temperature and salinity were averaged bi-weekly along the ice fronts of Denman and Scott Glaciers (see white dashed contour in figure 1a of the main manuscript text) along the sea floor. Data are provided as *.mat files. Note that the ocean model in Pelle et al. (2021) was run through 2100. In the control and low emission scenarios, we repreat the last 20-years of simulated ocean (2079-2099) conditions through 2300. In the high emission scenario, where a clear warming trend was evident in the ocean temperature after 2050, we extrapolate this warming trend with a square-root function and add this onto the retreated 2079-2099 repearted forcing. Channelized discharge flux data: Present-day output from the Glacier Drainage Systems (GlaDS) model in units of m3/s over grounded elements of the domain. Data is provided in a *.xyz file (see README.txt for instructions on how to load the data and interpolate onto model mesh using MATLAB). No processing has been applied other than subglacial flux values less than 0.001 m3/s have been removed from the dataset. Ice shelf basal melting is the primary mechanism driving mass loss from the Antarctic Ice Sheet, yet it is unknown how the localized melt enhancement from subglacial discharge will impact future Antarctic glacial retreat. Here, we develop a parameterization of ice shelf basal melt that accounts for both ocean and subglacial discharge forcing and apply it in future projections of Denman and Scott Glaciers, East Antarctica, through 2300. In forward simulations, subglacial discharge accelerates retreat of these systems into the deepest continental trench on Earth by 25 years. During this retreat, Denman Glacier alone contributes 0.33 mm/yr to global sea level rise, comparable to half of the contemporary sea level contribution of the entire Antarctic Ice Sheet. Our results stress the importance of resolving complex interactions between the ice, ocean, and subglacial environments in future Antarctic Ice Sheet projections. In this data publication, we present the model output and results associated with the following manuscript submitted to Science Advances: “Subglacial discharge will accelerate retreat of Denman and Scott Glaciers, East Antarctica”. We include yearly ice sheet model output between 2017-2300 for models that do and do not resolve subgalcial discharge in the melt calculation. We also include the ice sheet model's initial state. In addition, we include all ocean forcing time-series (temperature and salinity for the control, low emission, and high emission climate forcing scenarios) and the present-day chanellized subglacial discharge flux field over the Denman and Scott Glacier model domain, which are used as input into the melt parameterization. Lastly, we include a MATLAB script that contains the code used for ice shelf melt rate computation as well as a "README" file with further information on all data in this publication.  Ice sheet modeling results and initial states are compatible with the open source, NASA funded Ice-sheet and Sea-level System Model (ISSM, Larour et al. 2012), which is freely available for download here. In addition, the data files provided in the publication are available as *.mat files, which are compatible with MATLAB but can be accessed using most scripting languages.

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  • Authors: orcid bw Minter, Melissa;
    Minter, Melissa
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Minter, Melissa in OpenAIRE
    Hill, Jane; Dasmahapatra, Kanchon; Morecroft, Mike; +1 Authors

    # Mountain ringlet wing measurements [https://doi.org/10.5061/dryad.ttdz08m61](https://doi.org/10.5061/dryad.ttdz08m61) ## Description of the data This data set includes two spreadsheets of wing sizes for *Erebia epiphron* (forewing length/width and hindwing length/width) in field-caught contemporary material and museum specimens from the UK. "Contemporary_all" includes modern specimens caught from 19 populations in England and Scotland between 2018-2019, and "NHM_all" contains measures for museum specimens from England and Scotland between 1890 and 1987. Any NAs represent where measure was not possible, e.g. wing was missing or damaged. **Contemporary_all** This file includes all raw measures of contemporary wing length including: FW_length: length of forewing in mm averaged between the two forewings FW_width: width of forewing in mm averaged between the two forewings HW_length: length of hindwing in mm averaged between the two forewings HW_width: width of hindwing in mm averaged between the two forewings Region: either Lakes Lake District population or Scotland Population: Subpopulation where collected and corresponding 1km Grid Reference **NHM_all** all data are measured from photos from [https://data.nhm.ac.uk/](https://data.nhm.ac.uk/) This file includes all raw measures of museum wing length including: FW_length: length of forewing in mm averaged between the two forewings FW_width: width of forewing in mm averaged between the two forewings HW_length: length of hindwing in mm averaged between the two forewings HW_width: width of hindwing in mm averaged between the two forewings Locality: Nearest named location from museum label (NA represent where no locality information was present on museum label, beyond England/Scotland - see region) year: year of collection from museum label Region: Either England (Lake District) or Scotland Variation in insect size is often related to temperature during development, and may affect the persistence of populations under future climate warming if smaller individuals have reduced fitness. Montane species are particularly vulnerable to climate-driven local extinctions due to range retractions at their warm range margins, and so we examined spatial and temporal variation in body size in the butterfly Erebia epiphron in the UK, where it is restricted to two montane regions in England and Scotland. We examine spatial and temporal variation in body size in relation to temperature. We sampled 19 populations (6-15 individuals per population) in England and Scotland between 2018 and 2019 spanning elevations from 380-720 m, and examined museum specimens collected between 1890 and 1980. We examined individual body size (forewing length) and its relationship with the local temperature of sites, as well as temporal variation in body size over the last century in relation to the temperature during larval development. The forewing lengths of field-collected individuals in England were on average 7-8% smaller than in Scotland (England, mean = 14.9 mm, Scotland, mean = 15.9 mm), and warmer sites also had smaller individuals (0.13mm reduction in wing length per 1oC increase in local site mean temperature). However, we found no effect of temporal temperature variation on body size changes during larval development. E. epiphron were smaller in England than Scotland, and at warm range edge populations, which could have impacts on fecundity and dispersal ability. Future work should seek to understand the life-cycle lengths, genetics and phenotypic plasticity of these two populations to evaluate potential explanations for regional differences. In 2018 and 2019, we collected 6-15 male E. epiphron from each of 9 populations in England and 10 populations in Scotland representing a wide range of local elevation (380-780 m above sea level) and temperature gradients (5-7.6oC mean annual temperature). Wings were removed from individuals and electronically scanned. Photographs of specimens held at the Natural History Museum, London (2014) were downloaded (https://data.nhm.ac.uk/), and up to 5 males collected between 1890 and 1987 were measured (England n = 127 individuals, Scotland n = 100). We measured forewing length (distance between cell base and v10 wing margin veins; see Supplementary Materials SM1), a widely accepted proxy for body size, using the ‘draw line’ tool in Image J (https://imagej.nih.gov/ij/index.html). The length in pixels was converted into millimetres using the scale bar. Only wings where all veins and cell bases were visible were used for measurements. For each individual, both left and right forewings were measured and averaged. A measurement accuracy of ±0.3mm was estimated based on three replicate measurements of 10 random individuals.

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    Authors: orcid bw Journeaux, Katie;
    Journeaux, Katie
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    Journeaux, Katie in OpenAIRE
    Boddy, Lynne; Rowland, Lucy; Hartley, Iain;

    # Data from: A positive feedback to climate change: the effect of temperature on the respiration of key wood-decomposing fungi does not decline with time Katie L. Journeaux, Lynne Boddy, Lucy Rowland, Iain P. Hartley # Respiration data Respiration rate data for nine species of beech (Fagus sylvatica)-inhabiting white rot basidiomycetes decomposing wood exposed to 90 days of cooling to evaluate the medium-term effect of temperature on respiration. Columns: Species = Species of beech (Fagus sylvatica)-inhabiting white rot basidiomycetes Measurement = Stage of experiment and measurement number Day = Time (d) of measurement during the 241 d experiment Replicate = Identification of wood block microcosm replicates (species and number (1-20)) Species: Vc: Vuilleminia_comedens Ff: Fomes_fomentarius Cp: Chondrostereum_purpureum Tv: Trametes_versicolor Sh: Stereum_hirsutum Ba: Bjerkandera_adusta Hf: Hypholoma_fasciculare Pv: Phanerochaete_velutina Rb: Resinicium_bicolor Treatment = Wood block microcosms of each species were assigned to one of four temperature treatments (n = 5): pre-cooling (destructively sampled at 151 d, prior to cooling), cooled (incubated at 12 C at 151 d for 90 d), rewarmed (incubated at 12 C at 151 d for 60 d and then rewarmed to 20 C for 30 d) and control (incubated at 20 C for a further 90 d) Respiration_rate = Respiration rate (g C gdw-1 h-1) Cumulative_respiration = Cumulative respiration (mg C gdw-1) # Ergosterol data Ergosterol data for nine species of beech (Fagus sylvatica)-inhabiting white rot basidiomycetes decomposing wood exposed to 90 days of cooling to evaluate the medium-term effect of temperature on respiration. Columns: Species = Species of beech (Fagus sylvatica)-inhabiting white rot basidiomycetes Measurement = Stage of experiment and measurement number Day = Time (d) of measurement during the 241 d experiment Replicate = Identification of wood block microcosm replicates (species and number (1-20)) Species: Vc: Vuilleminia_comedens Ff: Fomes_fomentarius Cp: Chondrostereum_purpureum Tv: Trametes_versicolor Sh: Stereum_hirsutum Ba: Bjerkandera_adusta Hf: Hypholoma_fasciculare Pv: Phanerochaete_velutina Rb: Resinicium_bicolor Treatment = Wood block microcosms of each species were assigned to one of four temperature treatments (n = 5): pre-cooling (destructively sampled at 151 d, prior to cooling), cooled (incubated at 12 C at 151 d for 90 d), rewarmed (incubated at 12 C at 151 d for 60 d and then rewarmed to 20 C for 30 d) and control (incubated at 20 C for a further 90 d) Ergosterol = Ergosterol (g g wood-1) from pre-cooling (151 d), cooled, rewarmed and control treatments (241 d) Heterotrophic soil microorganisms are responsible for ~50% of the carbon dioxide released by respiration from the terrestrial biosphere each year. The respiratory response of soil microbial communities to warming, and the control mechanisms, remains uncertain, yet is critical to understanding the future land carbon (C)-climate feedback. Individuals of nine species of fungi decomposing wood were exposed to 90 days of cooling to evaluate the medium-term effect of temperature on respiration. Overall, the effect of temperature on respiration increased in the medium term, with no evidence of compensation. However, the increasing effect of temperature on respiration was lost after correcting for changes in biomass. These results indicate that C loss through respiration of wood-decomposing fungi will increase beyond the direct effects of temperature on respiration, potentially promoting greater C losses from terrestrial ecosystems and a positive feedback to climate change. Respiration rate and ergosterol data for nine species of beech (Fagus sylvatica)-inhabiting white rot basidiomycetes decomposing wood exposed to 90 days of cooling were collected to evaluate the medium-term effect of temperature on respiration. The data files can be opened using Microsoft Excel or R.

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    Authors: orcid bw Gauld, Jethro George;
    Gauld, Jethro George
    ORCID
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    Gauld, Jethro George in OpenAIRE
    Silva, João P.; Atkinson, Philip W.; Record, Paul; +47 Authors

    The full methodology to produce this data is described in Gauld et al. (2022) Hotspots in the grid: avian sensitivity and vulnerability to collision risk from energy infrastructure interactions in Europe and north Africa, Journal of Applied Ecology In brief: 65 Bird movement datasets containing high resolution GPS tracking data were downloaded from the www.movebank.org repository in April of 2019. These data were processed to remove locations associated with poor GPS accuracy and code locations in flight as present within a danger height band for wind turbines (15 - 135m above ground), Transmission Powerlines (10 - 60m above ground) or not. All datasets were combined into a single dataframe. This was overlaid onto a 5 x 5km fishnet grid covering Europe and North Africa, each grid cell had a unique NID value. For each species present within a given grid cell, the proportions of GPS locations in flight at danger height for the two danger height bands were calculated and weighted for uncertainty using the Wilson Confidence Interval, the resulting value for each grid cell was multiplied by the MBRCI (Morpho-Behavioural Conservation Status Risk Index) value to produce a sensitivity score for each species present in each grid cell where sufficient tracking data is available. To produce the family level sensitivity surface, the maximum sensitivity score of any species within a given family in a given grid cell is used. To produce the combined sensitivity surface, the maximum sensitivity score of any species within a given grid cell is used. The seasonal surfaces were produced in the same manner but calculated separately for Breeding and Non-Breeding periods. The vulnerability surface was produced by overlaying the sensitivity scores onto the density of either wind turbines or power lines in each grid cell. Grid cells were then categorised according to vulnerability by quantiles so Very Low: <0.025 percentile Low: 0.025 <0.25 percentile Moderate: 0.25 < 0.75 Percentile High: 0.75 < 0.975 Percentile Very High: >0.975 Percentile and No Data where GPS tracking data was not present. Wind turbine and power line densities were derived from the best available continental scale data at the time of the analysis. The accuracy of these datasets is discussed extensively in the supporting information of the paper. Raw data was processed in R, QGIS and ArcMap Wind turbines and power lines can cause bird mortality due to collision or electrocution. The biodiversity impacts of energy infrastructure (EI) can be minimised through effective landscape-scale planning and mitigation. The identification of high-vulnerability areas is urgently needed to assess potential cumulative impacts of EI while supporting the transition to zero-carbon energy. We collected GPS location data from 1,454 birds from 27 species susceptible to collision within Europe and North Africa and identified areas where tracked birds are most at risk of colliding with existing EI. Sensitivity to EI development was estimated for wind turbines and power lines by calculating the proportion of GPS flight locations at heights where birds were at risk of collision and accounting for species’ specific susceptibility to collision. We mapped the maximum collision sensitivity value obtained across all species, in each 5x5 km grid cell, across Europe and North Africa. Vulnerability to collision was obtained by overlaying the sensitivity surfaces with density of wind turbines and transmission power lines. Results: Exposure to risk varied across the 27 species, with some species flying consistently at heights where they risk collision. For areas with sufficient tracking data within Europe and North Africa, 13.6% of the area was classified as high sensitivity to wind turbines and 9.4% was classified as high sensitivity to transmission power lines. Sensitive areas were concentrated within important migratory corridors and along coastlines. Hotspots of vulnerability to collision with wind turbines and transmission power lines (2018 data) were scattered across the study region with highest concentrations occurring in central Europe, near the strait of Gibraltar and the Bosporus in Turkey. Synthesis and Applications: We identify the areas of Europe and North Africa that are most sensitive for the specific populations of birds for which sufficient GPS tracking data at high spatial resolution were available. We also map vulnerability hotspots where mitigation at existing EI should be prioritised to reduce collision risks. As tracking data availability improves our method could be applied to more species and areas to help reduce bird-EI conflicts. The results here are intended to provide a continental scale guide to where the collision risk hotspots are for the tracked birds used in the analysis and help guide further wind farms and power line development away from the higher risk areas for birds. It is important not to assume that areas where we do not have tracking data are free from risk, therefore this analysis does not remove the need for more local scale investigations into the ecological impact of a proposed development. 

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    Authors: orcid bw Marshall, Cicely;
    Marshall, Cicely
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Marshall, Cicely in OpenAIRE
    Wilkinson, Matthew; Hadfield, Peter; Rogers, Stephen; +18 Authors

    Study site Meadow establishment The meadow area covers about 40% of the original extent of the King’s College Back Lawn, which was first laid in 1772. The dimensions are 96 x 66 m lawn (0.63ha) and 96 x 37 m meadow (0.36 ha). A soil study commissioned before sowing showed both the topsoil (of 30 cm depth) and subsoil were strongly alkaline (pH 8.4) sandy loams. The topsoil had intermediate fertility (20-27 mg/l extractable phosphorus, 131-167 mg/l extractable potassium, 0.50-0.52% total nitrogen using Dumas method), whilst the subsoil had moderately high fertility (35-54 mg/l extractable phosphorus, 69-129 mg/l extractable potassium, 0.24-0.39% total nitrogen). Thus topsoil removal was not necessary, and seed was sown into glyphosate treated scarified topsoil at 6 g/m2 in October 2019. Three different seeds mixes sourced by Emorsgate were sown: the Great Lawn meadow mix, a perennial meadow species mix intended as the long-term flora of the meadow; a Cornfield Annual mix intended to provide first year colour; and a Supplementary Mix composed of species with lower establishment probability from seed, but high conservation value (Table S3). Meadow management The meadow is managed as an East Anglian hay meadow following traditional Lammas practices as far as possible. Hay is cut once a year around August 1st (Lammas day) to a height of c. 350 mm, with one subsequent cut at 350 mm in December, in place of the historical light grazing. Hand weeding was performed through the visitor seasons to remove the occasional individual of undesirable species (mainly Sonchus oleraceus and Cirsium vulgare). No other management or intervention has been practised. Management of the remaining 60% of the lawn continues as before; the lawn is a fine lawn mix with Agrostis stolonifera and Festuca rubra dominant. It is maintained with twice-weekly cuts from March–September, weekly cuts from October–December, dropping to biweekly cuts in January and February. NPK fertiliser is applied at c. 30 g/m2 in spring (8% N, 7% P, 8% K) and winter (3%N, 8% P, 8% K). A selective herbicide (Praxys) is applied to the remaining lawn at the minimum dosage once to twice per year. Insect pesticides are no longer applied, and watering is avoided as far as possible. Fertiliser and herbicide are applied in a directional fashion by ride-on vehicle during suitable weather conditions only to minimise run-off. Biodiversity The study has a before-after-control-impact (BACI) design for the plants, invertebrate, and nematode datasets, with sampling initiated before the meadow was established. Plants Botanical surveys were carried out in July for each flowering summer (2020, 2021) and in September for the pre-sowing baseline (2019). Five quadrats 50 × 50 cm were placed every 15 m perpendicular to the edge in both the meadow and the lawn (KBME01-KBME05, KBSO01-KBSO05). The origin of the meadow transect is 15 m from the northern lawn edge, and 5 m from the eastern lawn edge, at latitude 52.204691 °N, longitude 0.115580 °E. The origin of the lawn transect is 15 m from the southern lawn edge, and 5 m from the eastern lawn edge, at latitude 52.204045 °N, longitude 0.115737 °E. Abundance was measured by counting presence in each of 25 equal subdivisions of the quadrat. Mean plant height was recorded. In addition, running checklists of all species present in the lawn and meadow separately were collected over the course of each year, with 2-3 principal recording visits made each year in March, April, and July. Plants were identified as sown or non-sown using the stated seed mix (Table S3). Plant attribute data (distribution, scarcity) were sourced from PLANTATT (Hill et al., 2004). Designated species follow JNCC (2022). Invertebrates Above ground invertebrates were sampled by sweep net (July 2020, July 2021, pre-mowing) and pitfall trap (September 2019, 2020, 2021, post-mowing) at five points in both the meadow and lawn. Sweep net transects were 20 paces each, centred on the plant quadrat locations. Sweep net specimens were identified to species level for all taxa in 2020, employing morphospecies names as necessary, and to species for Hemiptera (bugs) and Araneae (spiders) only in 2021. 2020 data were restricted to Hemiptera and Araneae for analysis. Pitfall traps were sited at the centre of the plant quadrat locations. Pitfall trap specimens were weighed in 2021 only. Pitfall specimens were identified to species for all taxa present in 2019, and to species for Hemiptera, Araneae, and Orthoptera only in 2020 and 2021. Spider attribute data (hectad occurrence, habitat preferences) were sourced from British Spiders (2022). Hemiptera habitats were sourced from British Bugs (2022), with hectad distribution data from National Biodiversity Network (2022). Arthropod body size data were compiled from NatureSpot, British Bugs, BugGuide, and Bugwoodwiki (2022); male and female maximum body lengths were averaged. Designated species follow JNCC (2022). Bats Bats were surveyed via two unattended ultrasonic recorders (Wildlife Acoustics Song Meter SM4BAT FS Ultrasonic Recorder) placed adjacent to the meadow, and the lawn. Recorders were left for five or six nights each over four recording periods in May, June, July, and October in 2021 only. Audio files were auto-identified to species using Kaleidoscope version 5.4.6 before being checked manually. All records of Barbastella barbastellus were accepted, one record of Myotis bechsteinii was assigned to Myotis daubentonii, Plecotus austriacus records were assigned to Plecotus auritus or Eptesicus serotinus, one Rhinolophus ferrumequinum record was assigned to Pipistrellus pipistrellus. Myotis species are generally considered indistinguishable by audio recording only. The only Myotis species recorded in our dataset was auto-identified as Myotis daubentonii, which was also seen foraging at the river, and so the identity has been retained for analysis. The total number of echolocations recorded over the year in each habitat is used as a proxy for abundance (several passes by the same bat would not be distinguished). Designated species follow JNCC (2022). Soil nematodes Soils were sampled contemporaneously with the pitfall traps in September 2019, 2020, and 2021, and were co-located. Approximately 7 cm width by 10 cm depth of soil was dug and mixed. Nematodes were extracted by wetting 180-200g of soil on top of a paper towel with RO water. The wetted soil was left overnight in a tray covered with an autoclave bag to prevent evaporation. The flowthrough was collected in 1 L glass media bottles (Fisherbrand), and left to settle at a 45° angle for 24 hours. The sediment was pipetted into a 50 mL conical centrifuge tube (Corning) using a soda lime glass pipette (Fisherbrand) and centrifuged at 300 x RCF for 15 minutes. The pellet was transferred to a 1.5 mL microcentrifuge tube (Eppendorf) and centrifuged at 20 000 RCF for 1 minute and snap frozen in liquid nitrogen. The frozen tissue was lysed at 30 Hz in a tissue lyser (Qiagen) for two minutes with one 5mm and two 2mm glass beads (Qiagen). From the samples, DNA was extracted using a ChargeSwitch™ gDNA Micro Tissue Kit mini protocol. Using the well-established 18S RNA primers, NemFopt and 18Sr2bRopt (Waeyenberge et al., 2019) DNA was amplified (Q5® High-Fidelity DNA Polymerase) via PCR and cleaned using the Monarch® PCR & DNA Cleanup Kit 5 μg. The amplified DNA was sent to the GENEWIZ Takely Lab (UK) for next generation sequencing. Climate change Carbon sequestration Soil organic matter (SOM) was measured as a proxy for soil carbon sequestration. Soils were sampled contemporaneously with the pitfall traps in September 2019 and 2021, and were co-located. 7 cm width by 10 cm depth of soil was dug and mixed. For SOM, 100 g of soil from each sample was dried at 70°C for two days, homogenised and sieved (2 mm), then weighed into three pseudoreplicates of 5.00 g each per sample location. SOM was estimated using the loss on ignition method: samples were subjected to 8 hours in a muffle furnace at 450°C and reweighed once cool (Pribyl, 2010). SOM for the meadow and lawn samples were normally distributed and were compared using a t-test. We used a conversion factor of 2 (Pribyl, 2010) to convert from SOM to soil carbon i.e. soil organic matter is 50% carbon, and a literature value for soil density of 1440 kg/m3 for sandy loam (Yu et al. 1993). Above ground dry biomass was estimated for the meadow by counting the hay harvest in bales, weighing a bale, calculating the proportion of water in a bale by oven drying a sample, and multiplying up. These values are not included in the carbon sequestration figures as the pool is short-lived, nevertheless the productivity of the meadow is noted here. Society A survey was designed to assess respondents’ opinions of the cultural services provided by meadow and lawn, and respondents’ preferences for meadow and lawn (Appendix S1). Ethics oversight for the survey design and administration was provided by the Cambridge Hub. The survey was administered once in 2021 with responses recorded between 6th February and 26th March. At this time the meadow had had one flowering season and was in a winter dormant period. Given the timing and method of recruitment, respondents are likely to have seen the meadow for themselves, though we did not insist on this. 278 respondents were recruited via mailing lists of the University faculties, colleges, societies, and University affiliated organisations. Respondents were informed of the purpose, methods, and end use of the research and gave their informed consent to their data being collected and used for the purposes described in a privacy notice. An opt-out of having answers quoted was provided. No risks to participants were identified and participants were free to withdraw at any time. A small financial incentive was offered to respondents in the form of an Amazon gift voucher awarded to two randomly chosen respondents. Participants remained anonymous, unless they opted into being contacted for the randomly selected reward. All identifying information was deleted after disbursement of the rewards. Questions were always asked in the same order. The question of preference for lawn, meadow, or a mixture was repeated after the provision of information on the benefits of lawns and meadows. This information consisted of a written summary of the provisioning, regulating, cultural, and supporting ecosystem services derived from wildflower meadows and lawns, and was written by the survey administrator from published peer-reviewed literature. References to the primary sources were provided to participants. Responses were analysed using Wilcoxon signed rank tests, and chi-squared tests of association. Open responses were analysed by identifying and exploring common themes qualitatively. The biodiversity and climate crises are critical challenges of this century. Wildflower meadows in urban areas could provide important nature-based solutions, addressing the biodiversity and climate crises jointly, and benefitting society in the process. King’s College Cambridge (England, UK) established a wildflower meadow over a portion of its iconic Back Lawn in 2019, replacing a fine lawn first laid in 1772. We used biodiversity surveys, Wilcoxon signed rank, and ANOVA models to compare species richness, abundance, and composition of plants, spiders, bugs, bats, and nematodes supported by the meadow, and remaining lawn, over three years. We estimated the climate change impact of meadow vs lawn from maintenance emissions, soil carbon sequestration, and reflectance effect. We surveyed members of the university to quantify the societal benefits of, and attitudes towards, increased meadow planting on the collegiate university estate. In spite of its small size (0.36 ha), the meadow supported approximately three times more plant species, three times more spider and bug species and individuals, and bats were recorded three times more often over the meadow than the remaining lawn. Terrestrial invertebrate biomass was 25 times higher in the meadow compared with the lawn. Fourteen species with conservation designations were recorded on the meadow (six for lawn), alongside meadow specialist species. Reduced maintenance and fertilising associated with meadow reduced emissions by an estimated 1.36 Mg CO2-e per hectare per year compared with lawn. Relative reflectance increased by 25-34% for meadow relative to lawn. Soil carbon stocks did not differ between meadow and lawn. Respondents thought meadows provided greater aesthetic, educational, and mental well-being services than lawns. In open responses, lawns were associated with undesirable elitism and social exclusion (most colleges in Cambridge restrict lawn access to senior members of the college), and respondents proved overwhelmingly in favour of meadow planting in place of lawns on the collegiate university estate. This study demonstrates the substantial benefits of small urban meadows for local biodiversity, cultural ecosystem services, and climate change mitigation, supplied at a lower cost than maintaining conventional lawn.

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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