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140 results for “Data rescuing”
Data for Forb diversity globally is harmed by nutrient enrichment but can be rescued by large mammalian herbivory
Forbs (“wildflowers”) are important contributors to grassland biodiversity and services, but they are vulnerable to environmental changes that affect their coexistence with grasses. In a factorial experiment at 94 sites on 6 continents, we tested the global generality of several broad predictions arising from previous studies: (1) Forb cover and richness decline under nutrient enrichment, particularly nitrogen enrichment, which benefits grasses at the expense of forbs. (2) Forb cover and richness increase under herbivory by large mammals, especially when nutrients are enriched as grazing will release forbs from decreased grass competition under fertilization. (3) Forb richness and cover are less affected by nutrient enrichment and herbivory in more arid climates, because water limitation reduces the impacts of competition with grasses. We found strong evidence for the first, partial support for the second, and no support for the third prediction. Forb richness and cover are reduced by nutrient addition, with nitrogen having the greatest effect; forb cover is enhanced by large mammal herbivory, although only under conditions of nutrient enrichment and high herbivore intensity; and forb richness is lower in more arid sites, but is not affected by consistent climate-nutrient or climate-herbivory interactions. We also found that nitrogen enrichment disproportionately affects forbs in certain families (Asteraceae, Fabaceae). Our results underscore that anthropogenic nitrogen addition is a major threat to grassland forbs and the ecosystem services they support, but grazing under high herbivore intensity can offset these nutrient effects. For associated r code that goes along with this dataset, please refer to the following Zenodo repository: https://zenodo.org/records/14207290
Data from: How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue
<p>Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation, and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity, and the fixation of maladaptive alleles, hindered adaptation, and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.</p>
Data generated for the publication: Keeping it in the family: Using protein family templates to rescue poor AlphaFold models unliked
<p>Data and manuscript of:</p> <p>Keeping it in the family: Using protein family templates to rescue low confidence AlphaFold2 models</p> <p>Francesco Costa1, Matthias Blum1 and Alex Bateman1</p> <ol> <li>European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton. CB10 1SD. UK</li> </ol> <ul> <li>results contains the workflow results;</li> <li>AF2_seed contains results of the comparison with AF2 run with multiple seeds;</li> </ul>
Data from: Understanding evolutionary rescue and parallelism in response to environmental stress
<p>Evolutionary rescue, the process by which populations facing environmental stress avoid extinction through genetic adaptation, is a critical area of study in evolutionary biology. The order in which mutations arise and get established will be relevant to the population's rescue. This study investigates the degree of parallel evolution at the genotypic level between independent populations facing environmental stress and subject to different demographic regimes. Under density regulation, two regimes exist: in the former, the population can restore positive growth rates by adjusting its population size or through adaptive mutations, whereas in the second regime, the population is doomed to extinction unless a rescue mutation occurs. Analytical approximations for the likelihood of evolutionary rescue are obtained and contrasted with simulation results. We show that the initial level of maladaptation and the demographic regime significantly affect the level of parallelism. There is an evident transition between these two regimes. Whereas in the first regime, parallelism decreases with the level of maladaptation, it displays the opposite behavior in the rescue/extinction regime. These findings have important implications for understanding population persistence and the degree of parallelism in evolutionary responses as they integrate demographic effects and evolutionary processes.</p>
Data from: The limit to evolutionary rescue depends on ploidy in yeast exposed to nystatin
<h4>Data package about haploid vs diploid adaptation to nystatin in yeast</h4> <div>This package contains files and scripts related to the publication "The limit to evolutionary rescue depends on ploidy in yeast exposed to nystatin". Raw data and analyses are included.</div>
Supplement to "Discriminating non-stationary flood hazard effects via probabilistic estimation of sparse residuals from rescued and rated stage–discharge data"
<p><span>This supplement contains the data and script associated with “Sparse hydrometric data rescue for exploratory analyses of conveyance-driven flood hazard trends and controls,” which has been submitted to a journal for consideration. This supplement is deposited on Zenodo.</span></p> <p><span> </span></p> <p><strong><span>Code, Data, and Attribute Descriptions</span></strong></p> <p><span> </span></p> <p><span>Uploaded are five directories (indicated in <strong>bold </strong>font, with their contents detailed below) containing input data and various outputs of the analyses performed for our case study on the Pulangi River at Lumayong (Philippines). Please consider this description equivalent to an omnibus README file for the deposited files. Note that we collected and rescued the hydrometric data herein from the archives of the Water Projects Division (WPD) of the Philippine Department of Public Works and Highways (DPWH).</span></p> <p><span> </span></p> <p><span><span>●<span> </span></span></span><strong><span>Pln_R_code </span></strong><span>contains (1) <em>Pln_RC.R, </em>the <em>R</em> script file, and (2) <em>240601_Pln_RC.RData</em>, which stores objects generated from the script based on the last execution (on 1 June 2024). The script consists of admittedly too many lines of code (e.g., for data wrangling, formal analyses, and producing figures used in the manuscript) that should have been split into multiple .R files. Apologies. Please be guided by the outline and the comments and kindly reach out lest issues with the code arise.</span></p> <p><strong><span> </span></strong></p> <p><span><span>●<span> </span></span></span><strong><span>Pln_GH_PDFs </span></strong><span>contains the scanned gaugekeeper’s reports of gauge heights (“stages”).<span> </span></span></p> <p><span><span>○<span> </span></span></span><span>The name of each file varies. For example, <em>Pln-GH-1100.pdf </em>contains daily stage readings for all months in the year 2011. But, if the last two digits are not zeroes, as in <em>Pln-GH-1204.pdf</em>, they refer to the month of that year; in this case, for example, the PDF file contains stage data for the month of April in the year 2012.</span></p> <p><span><span>○<span> </span></span></span><span>Each scanned sheet contains sub-daily (with readings at “AM,” “NOON,” and “PM”) and mean daily stages for a given month. </span></p> <p><span><span>○<span> </span></span></span><span>Also indicated are the gaugekeeper’s remarks on the daily weather (e.g., “fair,” “cloudy”). Under inclement weather, the gaugekeeper would note the duration of rainfall and its intensity and might, at times, record extra stage readings. </span></p> <p><span> </span></p> <p><span><span>●<span> </span></span></span><strong><span>Pln_DM_PDFs </span></strong><span>contains the following PDF files and a sub-directory:</span></p> <p><span><span>○<span> </span></span></span><em><span>Pln-DM-<XXXXXX>.pdf </span></em><span>contains the scanned log for each direct stage—discharge measurement, otherwise known as “gaugings.” Each file is named according to the date of gauging (YYMMDD). For example, the log for the gauging performed on 24 February 2011 can be found in the file <em>Pln-DM-110224.pdf. </em>Data from these gaugings are summarized in <em>Pln-DM-filtered.csv</em> in the <strong>Pln_In_CSVs</strong> directory.</span></p> <p><span><span>■<span> </span></span></span><span>The readability of each file varies based on the original quality of the original paper-format data. </span></p> <p><span><span>■<span> </span></span></span><span>The first page in each file contains, on the left side, a summary of the gauging data and metadata (e.g., date of measurement, number of gauging verticals or “sections” used, method of crossing or measuring the cross-section), and on the right side, the velocity—area readings at each gauging vertical. </span></p> <p><span><span>■<span> </span></span></span><span>The second page in each file contains the plotted cross-section of the channel at the time of measurement. </span></p> <p><span><span>○<span> </span></span></span><em><span>Pln-RC.pdf </span></em><span>contains various other paper-format data and some annotations relevant to the historical rating at the station. </span></p> <p><span><span>■<span> </span></span></span><span>P. 1: The hydrographic engineer’s comment (dated 19 January 2012) on the evaluation of the discharge data, specifically detailing the periods of validity of the rating curves developed for different sub-periods of monitoring.</span></p> <p><span><span>■<span> </span></span></span><span>PP. 2—3: A summary table of all the gaugings performed from 1983 to 2010 whose logs were no longer retrievable (and hence not included as a DM-PDF file in this directory). </span></p> <p><span><span>■<span> </span></span></span><span>PP. 4—5: Plots of the official stage—discharge rating curves developed by DPWH hydrographers.</span></p> <p><span><span>■<span> </span></span></span><span>PP. 6—8: Rating tables used to convert daily mean stages to deterministic discharge estimates. Two of these rating tables were digitized and can be found in the <strong><em>Pln_RatingTables</em></strong> sub-directory in <strong>Pln_In_CSVs.</strong></span></p> <p><span><span>■<span> </span></span></span><span>PP. 9—22: Daily stage (m) and its corresponding daily discharge (L/s) for the 2004—2010 sub-period. The stages were digitized and included in <em>Pln-H_arch.csv </em>in the <strong>Pln_In_CSVs </strong>directory.</span></p> <p><span><span>○<span> </span></span></span><strong><em><span>Pln_DM_unused </span></em></strong><span>contains the PDF files of logs (including data and metadata) corresponding to the gaugings that were excluded from our analysis following our filtering step for gauging location consistency.</span></p> <p><strong><span> </span></strong></p> <p><span><span>●<span> </span></span></span><strong><span>Pln_In_CSVs </span></strong><span>contains the following CSV files and two sub-directories; these files were used as inputs to the <em>R </em>script for formal analyses:</span></p> <p><span><span>○<span> </span></span></span><em><span>Pln-H_arch.csv </span></em><span>contains the mean daily stage values [“H_bar_arch”] (m) for every day in the 2004—2020 sub-period [“Date”] (YYYY-MM-DD). The stages in this file are already corrected for gross errors.</span></p> <p><span><span>○<span> </span></span></span><em><span>Pln-DM-filtered.csv </span></em><span>contains the following information on the gaugings performed on the Pulangi at Lumayong (1983—2020): (i) date [“Date”] (YYYY-MM-DD); (ii) stage [“H”] (m); (iii) discharge in L/s [“Q_lps”] and m<sup>3</sup>/s [“Q_cms”]; (iv) wetted area [“A_sqm”] (m<sup>2</sup>); (v) mean flow velocity [“Vel_mps”] (m/s); (vi) channel width [“W_m”] (m); (vii) mean flow depth [“D_ave_m”] (m); (viii) location of the measurement cross-section with respect to the staff gauge, with negative values meaning downstream of the gauge and positive values meaning upstream of the gauge [“XS_loc_wrt_gage”]; (ix) maximum flow depth [“Max_Depth_m”] (m); and (x) minimum streambed elevation [“MINSBE”] (m). Note that this file includes only gaugings that passed our filtering step for measurement location consistency. </span></p> <p><span><span>○<span> </span></span></span><em><span>Pln-DM_oview.csv </span></em><span>contains information on the temporal coverage (bounded by “Start_date” and “End_date”) of the available hydrometric data from the station archives.</span></p> <p><span><span>○<span> </span></span></span><em><span>Pln-H_sample_GrossE_corr.csv </span></em><span>contains a sample sub-period (2017-06-15 through 2017-11-30) and the corresponding values of stages (m), uncorrected [“H_uncorrected”] and corrected for gross errors [“H_corrected”]. This CSV file was used as an input to the <em>R </em>script to produce one of the figures in the manuscript.<span> </span></span></p> <p><span><span>○<span> </span></span></span><em><span>Pln-XS-csv.csv</span></em><span> contains data on the gauging transects: gauging ID [“GaugingID”]; date [“Date”] (YYYY-MM-DD); lateral distance from a fixed initial point [“Lat_distance”] (m); width of the gauging vertical [“Width_vert..m.”] (m); depth relative to the water surface [“Depth..m.”] (m); stage at the gauging vertical [“H_m_vert..m.”] (m); and elevation with respect to a fixed arbitrary datum at the station [“Elev..m.”] (m).</span></p> <p><span><span>○<span> </span></span></span><strong><em><span>Pln_Rating_Tables</span></em> </strong><span>contains two rating tables, <em>Pln-DM - RatingTable_A.csv </em>and <em>Pln-DM - RatingTable_B.csv</em>, prepared and used by DPWH hydrographers for converting stage values to deterministic discharge estimates for the Pulangi River at Lumayong for the 1980s—early 2000s sub-period. Each rating table contains columns for stage [“H”], discharge in L/s [“Q_lps”], and discharge in m<sup>3</sup>/s [“Q_cms”].</span></p> <p><span><span>○<span> </span></span></span><strong><em><span>Pln_Q_rated </span></em></strong><span>contains two CSV files: <em>Pulangi.csv </em>has the columns “YEAR”, “DAY”, and every month of the year [“JAN” through “DEC”] for the 1983—2003 sub-period, with the values under each month column indicating the deterministic discharge estimates in L/s; <em>Pulangi_trunc.csv </em>contains similarly formatted data, but for the 2009—2010 sub-period.</span></p> <p><span><span> </span></span></p> <p><span><span>●<span> </span></span></span><strong><span>Pln_Out_CSVs </span></strong><span>contains two CSV files, the primary outputs of the hydrometric data rescue effort. </span></p> <p><span><span>○<span> </span></span></span><em><span>Pln_Q_recon.csv </span></em><span>contains the reconstructed discharge time series (1983—2020) and its associated uncertainties at the 95% credibility interval. It has the following columns: date [“Date”] (YYYY-MM-DD); daily stage [“H”] (m); lower bound of the discharge estimate [“Q_lwr”] (m<sup>3</sup>/s); median discharge estimate [“Q_med”] (m<sup>3</sup>/s); and the upper bound of the discharge estimate [“Q_upr”] (m<sup>3</sup>/s).</span></p> <p><em><span>Pln_H_recon.csv </span></em><span>contains the reconstructed and quality-controlled stage time series (1983—2020). It has the following columns: date [“Date”] (YYYY-MM-DD); mean daily stage that has been corrected for gross errors, but not yet filtered through other quality checks [“H_bar_arch”] (m); quality check for low outliers [“Low_Outlier”] (TRUE/FALSE); quality check for flatliners [“Flatliner”] (TRUE/FALSE); difference between the stage values on day <em>i </em>and day <em>i-1</em> [“Daily_Step”] (m); quality check for large steps [“Large_Step”] (TRUE/FALSE); and the corrected and quality-controlled mean daily stage values [“H”] (m).</span></p>
Data for "Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations"
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Code and initial metapopulation data for model construction and simulation analyses for: Genetic rescue from protected areas is modulated by migration, hunting rate and timing of harvest
<p>Migrants from protected areas may buffer the risk of harvest-induced evolutionary changes in exploited populations that face strong selective harvest pressures in both terrestrial and marine ecosystems. Understanding the mechanisms favouring genetic rescue through migration could help ensure sustainable harvest outside protected areas and conserve genetic diversity inside those areas. We developed a stochastic individual-based metapopulation model to evaluate the potential for migration from protected areas to mitigate the evolutionary consequences of selective harvest. We parameterized the model with detailed data from individual monitoring of two populations of bighorn sheep subjected to trophy hunting. We tracked horn length through time in a metapopulation including large protected and trophy-hunted populations connected through male breeding migrations. We quantified and compared declines in horn length and rescue potential under various combinations of migration rate, hunting rate in hunted areas and temporal overlap in timing of harvest and migrations, which affects the migrants' survival and chances to breed within exploited areas. Our simulations suggest that the effects of size-selective harvest on male horn length in hunted populations can be dampened or avoided if harvest pressure is low, migration rate is substantial, and migrants have a low risk of being shot. Intense size-selective harvest impacts the phenotypic and genetic diversity in horn length, and population structure through changes in proportions of large-horned males, sex ratio and age structure. When hunting pressure is high and overlaps with male migrations, effects of selective removal also emerge in the protected population, so that instead of a genetic rescue of hunted populations, our model predicts undesirable effects inside protected areas. Our results stress the importance of a metapopulational approach to management, to promote genetic rescue from protected areas and limit ecological and evolutionary impacts of harvest on both harvested and protected populations.</p>
Data and code from: Longevity hinders evolutionary rescue through slower growth but not necessarily slower adaptation
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Data from: The potential for evolutionary rescue in an arctic seashore plant threatened by climate change
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Data from: Understanding evolutionary rescue and parallelism in response to environmental stress
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Data from: Maintaining local adaptation is key for evolutionary rescue and long-term persistence of populations experiencing habitat loss and a changing environment
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Data from: The effects of hybridization and parasite infection on predation survival and behaviour of endangered landlocked salmon – implications for genetic rescue
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Data for: Predicting the contribution of single trait evolution to rescuing a plant population from demographic impacts of climate change
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Code and initial metapopulation data for model construction and simulation analyses for: Genetic rescue from protected areas is modulated by migration, hunting rate and timing of harvest
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Data from: A test for microbiome-mediated rescue via host phenotypic plasticity in Daphnia
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Data from: Evolutionary rescue of freshwater copepods during historical lake acidification
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Codes and data from: The effect of habitat choice on evolutionary rescue in subdivided populations
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Data from: Rescue or murder? The effect of prey adaptation to the predator subjected to fisheries
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Data from: How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue
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