Find research datasets worth reusing

openother-openNov 2022View details →

Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States

This repo includes supporting material for the publication: Qiu, M., Zigler, C. M., & Selin, N. E. (2022). Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States. Science Advances, 8(48), eabn8762. Please download and unzip the file "<a href="https://zenodo.org/api/files/bef77248-ca65-4ae6-9a92-ad00fc068665/mhqiu/wind_pollution_EJ-v1.0.zip?versionId=a9b5b8e9-273c-4431-a101-4f9a4c331675">mhqiu/wind_pollution_EJ-v1.0.zip</a>". Please see README for a full description of the sample data included in this repo.  README 1. Regression results:  EGU_regression_scenario_results.xlsx  It includes regression results for each EGU in our sample (1264 EGUs in total).  2. Air quality simulation results: 2.1 GEOS-Chem simulation GC_daily_pm25_o3_scenarios.nc  It contains surface level annual mean PM2.5 and MDA8 O3 concentration under different emission scenarios. We include four scenarios in total: - baseline scenario: air quality **without** the amount of wind power associated with 2014 RPS targets  - expost scenario:  air quality with the wind power associated with 2014 RPS targets under the current dispatch decisions - health damage minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the health damage - CO2 minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the CO2 emissions Therefore, to calculate the air quality impacts of wind power under different dispatch decisions:   current (ex post) = ex post - baseline.   health damage minimizing =  health damage minimizing - baseline.   CO2 minimizing scenario = CO2 minimizing - baseline.   2.2 InMAP simulations InMAP/xx.shp   The shapefiles contain annual mean PM2.5 concentration simulated with InMAP under different emission scenarios.   baseline.shp: baseline scenario   ex post.shp: ex post scenario   health_damage_minimizing.shp: health damage minimizing scenario   co2_minimizing.shp: CO2 minimizing scenario Descriptions of the four scenarios are the same as above for GEOS-Chem. 3. County-level air quality change for different demographic groups (GEOS-Chem) county_pm_o3_changes_by_groups_GEOS_CHEM.xlsx   This file contains changes in county-level simulated PM2.5 and O3 concentrations due to wind power under different scenarios. It also includes the total population at the county level and the population for each subgroup. This data can be used to calculate the distributional effects of air quality benefits across different population groups.

opencc-by-4.0Apr 2022View details →

Wind Data for Station-wise assessment of wind speed and direction under future climates across the United States

This study employs statistical techniques to evaluate climate model performance in wind speed and direction and their projected future changes under the representative concentration pathway (RCP) 8.5 scenario over inland and offshore across the Continental United States (CONUS). It extends the scope of existing studies by characterizing the changes of the full range of the joint wind speed and direction distribution via a conditional approach. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the statistical significance of the future projections. The proposed conditional approach provides a better way to characterize the directional wind speed distributions that offers additional insights for the joint assessment of speed and direction.  WRF data: We focus on seasonal (December-January-February (winter hereafter) and June-July-August (summer hereafter) statistics computed from the 3-hourly RCM outputs on both wind speed and direction over ten locations with different local topological features. We use three WRF simulations driven by Community Climate System Model 4 (CCSM4), the Geophysical Fluid Dynamics Laboratory Earth System Model 2 (GFDL-ESM2G), and the Hadley Centre Global Environment Model version 2 (HadGEM2-ES). These three GCMs represent a range of climate sensitivities that encompasses most of the coupled model intercomparison project phase 5 (CMIP5) GCMs when projecting future temperature changes. In this work, we focus on RCP 8.5 scenario for future projections. A 16-member ensemble of one-year of RCM simulation using bias corrected CCSM-driven WRF is also generated for analyzing the uncertainty due to the RCM's internal variability (IV).  Benchmark data: Reanalysis data are used as a verification dataset in order to evaluate the RCMs' wind conditions under study for the historical time period. For the seven inland locations, we use the second phase of the  multi-institution North American Land Data Assimilation System project, phase 2, at a spatial resolution of 12 km and hourly resolution. NLDAS-2 is an offline data assimilation system featuring uncoupled land surface models driven by observation-based atmospheric forcing. The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are at a 32-km spatial resolution and 3-hourly temporal frequency. In-situ measurement: Since reanalysis data can present errors and uncertainties, ground measurements and offshore buoy measurements are used to consolidate the evaluation of RCMs' wind conditions for inland and offshore locations in historical climates. Observational data are extracted from the Automated Surface Observing System (ASOS) network that consists stations covers the U.S. territory, available at ftp://ftp.ncdc.noaa.gov/pub/data/asos-onemin. The offshore downscaled wind speeds from the historical decade are compared with National Data Buoy Center (NDBC) buoy observations of near-surface wind velocities available at https://www.ndbc.noaa.gov. The observed winds at the NBDC anemometers are adjusted to 10-m above ground height and at 3-hourly rate.

opencc-zeroApr 2022View details →

Data from: Breeding heat tolerant orchardgrass germplasm for summer persistence in high temperature stress environments of the southeastern United States

This is digital research data corresponding to a published manuscript, Breeding heat tolerant orchardgrass germplasm for summer persistence in high temperature stress environments of the southeastern United States, in Crop Science, Volume 61, p. 1915 - 1925. Orchardgrass (Dactylis glomerata L.) could serve as a cool-season perennial in southeastern production systems, but often does not behave as a true perennial under high temperature stress conditions of the region. This work sought to develop heat-tolerant orchardgrass germplasm through recurrent phenotypic selection (RPS) that would both reduce secondary seed dormancy caused by high soil temperatures and improve stand persistence over summer months. Selection was conducted in a growth chamber 40/30 °C (12/12 h, light/darkness), with germinated seedlings subjected to an additional 2–3 weeks of 40/30 °C conditions. The base germplasm (Cycle 0) and selected individuals (Cycles 1–3) were transplanted into the field, then harvested for seed. Forty-degree germination tests compared mean cumulative germination, velocity of germination within 8 days (VOG8), and realized heritability. Stand persistencewas assessed 1 year after transplanting.

opencc-by-4.0May 2022View details →

Mobile Synthesis Unit (MOBSU) plant

This video describes the ICO2CHEM concept for delivering synthetic fuels and chemicals from waste CO2 and H2, and shows the MOBSU plant running in the Industrial Park Infraserv Hoechst in Frankfurt am Main, Germany.

New non-native pseudocryptic Cyclorhipidion species (Coleoptera: Curculionidae: Scolytinae: Xyleborini) found in the United States as revealed in a multigene phylogeny

Pseudocryptic species, those that are difficult to diagnose using traditional taxonomic methods, are serious impediments for recognizing the introduction of non-native species. Rapid identification of species facilitates a rapid response to newly introduced species which can lessen their damaging effects. This situation is acute for known pest species such as xyleborine ambrosia beetles which are difficult to identify given minute morphological, often variable, diagnostic characters. These beetles have been introduced into non-native temperate regions and have caused economic and ecological havoc. In this study, we produced DNA-based phylogenies using four genes for individuals of Cyclorhipidion bodoanum (Reitter, 1913), C. distinguendum (Eggers, 1930), and C. pelliculosum (Eichhoff, 1878) sampled from their introduced and native Asian ranges and as well as other Cyclorhipidion species. In addition, we review subtle morphological characters for diagnostic potential for these similar species. Bayesian phylogenetic analysis produced well-resolved and supported phylogeny that provided evidence for multiple introductions of C. bodoanum and C. distinguendum into the US and the occurrence of pseudocryptic species. The ambrosia beetles Cyclorhipidion tenuigraphum (Schedl, 1953) and C. nemesis Smith & Cognato, sp. nov. are reported in North America for the first time. We find that the pattern of elytral interstrial setae is an unrealized source for the identification of Cyclorhipidion species. This study resulted in the recognition of six species adventive to the US with the revised status of C. californicum (Wood, 1975). All species known from North American are diagnosed, illustrated and a key is provided.

Combining point counts and autonomous recording units improves avian survey efficacy across elevational gradients on two continents

Accurate biodiversity and population monitoring is a requirement for effective conservation decision-making. Survey method bias is therefore a concern, particularly when research programs face logistical and cost limitations. We employed point counts (PCs) and autonomous recording units (ARUs) to survey avian biodiversity within comparable, high elevation, temperate mountain habitats at opposite ends of the Americas: 9 mountains in British Columbia (BC), Canada and 10 in southern Chile. We compared detected species richness against multi-year species inventories and examined method-specific detection probability by family. By incorporating time costs, we assessed the performance and efficiency of single vs. combined methods. Species accumulation curves indicate ARUs can capture ~93% of species present in BC but only ~58% in Chile, despite Chilean mountain communities being less diverse. The avian community, rather than landscape composition, appears to drive this dramatic difference. Chilean communities contain less-vocal species, which ARUs missed. Further, 6/13 families in BC were better detected by ARUs while 11/11 families in Chile were better detected by PCs. Where survey conditions differentially impacted method performance, PCs mostly varied over the morning and with canopy cover in BC, while ARUs mostly varied seasonally in Chile. Within a single year of monitoring, neither method alone was predicted to capture the full avian community, with the exception of ARUs in the alpine and subalpine of BC. PCs contributed little to detected diversity in BC, but including this method resulted in negligible increases in total time costs. Combining PCs with ARUs in Chile significantly increased species detections, again, for little cost. Combined methods were among the most efficient and accurate approaches to capturing diversity. We recommend conducting point counts while ARUs are being deployed and retrieved in order to capture additional diversity with minimal additional effort and to flag methodological biases using a comparative framework.

Data from: When can model-based estimates replace surveys of wildlife populations that span many discrete management units?

Monitoring widely distributed species on a budget presents challenges for the spatio-temporal allocation of survey effort. When there are multiple discrete units to monitor, survey alternatives such as model-based estimates can be useful to fill information-gaps but may not reliably reflect biological complexity and change. The spatio-temporal allocation of survey effort that minimizes uncertainty for the greatest number of units within a budget can help to ensure monitoring efforts are optimized. We used aerial survey-based population estimates of moose (Alces alces) across 30 Wildlife Management Units (WMUs) in Ontario, Canada to parameterize simulated populations and test the performance of different monitoring scenarios in capturing WMU-specific annual variation and trends. Firstly, we tested scenarios that prioritized conducting a survey for a unit based on one of three management criteria: population state, population uncertainty, or number of years between surveys. Also incorporated in the decision framework were WMU-specific costs and annual budget constraints. Secondly, we tested how using model-based estimates to fill information-gaps improved population and trend estimates. Lastly, we assessed how the utility (based on minimizing population uncertainty) of using a model-based estimate rather than conducting a survey was impacted by population density, severity of environmental stressors, and years since the last survey. Interval-based monitoring that minimized the number of years between surveys captured accurate trends for the highest number of WMUs, but annual variation was poorly captured regardless of management criteria prioritized. Using model-based estimates to fill information gaps improved trend estimation. Further, the utility of conducting a survey increased with time since the last survey and was greater for populations with low densities when the severity of environmental stressors was high, while being greater for populations with high densities when environmental severity was low. Overall, the utility of aerial survey monitoring was strongly associated with WMU-specific monitoring precision and the predictive power of model-based estimates. If long-term trends are evident then there is greater value in using alternatives such as model-based predictions to replace surveys, but model-based estimates may be a poor substitute when there is strong annual variation and when using a simple model.

Data from: Land-based climate solutions for the United States

Meeting end-of-century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature-based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature-based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the U.S. that illustrates the benefits of combining approaches. We assign a portion of non-forested former cropland to bioenergy sufficient to meet projected mid-century transportation needs, with the remainder assigned to nature-based solutions such as reforestation. Bottom-up mitigation potentials for the aggregate contributions of crop, grazing, forest, and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost-effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of reforestation. Results show a conservative end-of-century mitigation capacity of 110 (57 – 178) Gt CO2e for the U.S., ~50% higher than existing estimates that prioritize nature-based or bioenergy solutions separately. Further research is needed to shrink uncertainties but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now.

opencc-zeroJun 2022View details →

The biome inventory – standardizing global biogeographical land units

The subdivision of the Earth's terrestrial surface into different biomes, ecozones, bio-climatic realms or other large ecological land units is an essential reference for global biogeographical and ecological studies. Various classification schemes exist. They differ significantly in terms of the considered criteria for classification and the underlying methodology of class assignments. Evident divergences between global biome concepts are elusive, weakening hereon based analyses and assumptions. Compilation and standardization are essential for obtaining a framework that enables the comparison of different products. To address this need, we created a catalogue of standardized categorial maps comprising 31 different global products based on various methodological approaches. Those were processed individually to facilitate their use in large-scale biogeographical and ecological analyses.

opencc-by-4.0Jun 2022View details →

Cascading drought-heat dynamics during the 2021 Southwest United States Heatwave

In June of 2021 the southwest United States experienced a record-breaking heatwave. This heatwave came at a time when the region was in severe drought. As drought alters the surface energy budget in ways that affect lower atmosphere temperature and circulations, it is possible that the combined drought-heat event was a cascading climate hazard, in which preexisting drought exacerbated the heatwave. We apply satellite observation and numerical experiments with the Weather Research and Forecasting (WRF) model to test for land-atmosphere feedbacks during the heatwave consistent with drought influence. We find a modest positive drought-heat effect, as WRF simulations that include the drought have marginally higher air temperatures than those that exclude the initial drought conditions, with more substantial effects in wetter, forested areas. Evidence of drought-heat-drought coupled feedbacks was similarly modest in our simulations, as accounting for drought preconditioning led to a small reduction in simulated precipitation in the region. The files in this dataset are the output of our analyses and numerical modeling presented in this study.

opencc-zeroJun 2022View details →

Neural processing and perception of Schroeder‐phase harmonic tone complexes in the gerbil: Relating single‐unit neurophysiology to behavior

Schroeder-phase harmonic tone complexes have been used in physiological and psychophysical studies in several species to gain insight into cochlear function. Each pitch period of the Schroeder stimulus contains a linear frequency sweep; the duty cycle, sweep velocity, and direction are controlled by parameters of the phase spectrum. Here, responses to a range of Schroeder-phase harmonic tone complexes were studied both behaviorally and in neural recordings from the auditory nerve and inferior colliculus of Mongolian gerbils. Gerbils were able to discriminate Schroeder-phase harmonic tone complexes based on sweep direction, duty cycle, and/or velocity for fundamental frequencies up to 200 Hz. Temporal representation in neural responses based on the van Rossum spike-distance metric, with time constants of either 1 ms or related to the stimulus' period, was compared to average discharge rates. Neural responses and behavioral performance were both expressed in terms of sensitivity, d', to allow direct comparisons. Our results suggest that in the auditory nerve, stimulus fine structure is represented by spike timing while envelope is represented by rate. In the inferior colliculus, both temporal fine structure and envelope appear to be represented best by rate. However, correlations between neural d' values and behavioral sensitivity for sweep direction were strongest for both temporal metrics, for both auditory nerve and inferior colliculus. Furthermore, the high sensitivity observed in the inferior colliculus neural rate-based discrimination suggests that these neurons integrate across multiple inputs arising from the auditory periphery.

opencc-by-4.0Jun 2022View details →

MIDAS2 unit testing

MIDAS2 unit test and custom genome collection template. Github: https://github.com/czbiohub/MIDAS2

opencc-by-4.0Jul 2022View details →

Distance matrix for UNITE sequences

Flattened and compressed version of 2 pairwise distance matrices corresponding to <a href="https://doi.org/10.15156/BIO/1264708">this</a> UNITE release. Can be imported in a pipeline for building an overarching fungal phylogeny for UNITE (<a href="https://github.com/luukromeijn/MDDB-phylogeny">Github source code</a>).  Importing might take up to an hour, but this is still much quicker than the 36-hours pairwise distance calculation.  The 6-mer Google distance is way more accurate than the w-metric, so we highly recommend using the 6-mer Google distance. More documentation can be found <a href="https://github.com/luukromeijn/MDDB-phylogeny#readme">here</a>.

opencc-by-4.0Mar 2022View details →

Juno Stellar Reference Unit (SRU) image data from Juno's G34 Ganymede flyby on 7 June 2021

This is the Juno Stellar Reference Unit (SRU) image data from Juno's G34 Ganymede flyby on 7 June 2021. The image is discussed in the paper, "Surface features of Ganymede revealed in Jupiter-shine by Juno's Stellar Reference Unit," published in AGU Geophysical Research Letters.

opencc-by-4.0Jul 2022View details →

Artefact to our paper "An Empirical Study of Automated Unit Test Generation for Python"

Artefact for “An Empirical Study of Automated Unit Test Generation for Python” Together with our paper “An Empirical Study of Automated Unit Test Generation for Python”, we provide this artefact for future use. Pynguin Version We used Pynguin 0.25.2 for our experiments. The releases of Pynguin are archieved by Zenodo, too. Pynguin 0.25.2 is available under DOI <a href="https://doi.org/10.5281/zenodo.6836225">10.5281/zenodo.6836225</a>. Preparation of the Environment We use the <a href="https://python-poetry.org"><code>poetry</code></a> dependency-management tool to manage all dependencies for this artefact. Install this tool if you have not done yet. Furthermore, let <code>poetry</code> create a virtual environment for the experiment by execution <code>poetry install</code>. Execution of the Experiment The execution scripts make several assumptions that are based on our infrastructure. We maintain a SLURM cluster infrastructure that defines different <code>constraints</code> for different machines. Furthermore, we assume some paths to be present: we assume every computing machine to have writable mount points at <code>/local/${USER}</code> and <code>/local/hdd/${USER}</code>. On our machines, both are mount points on the local hard disk/SSD of the computing machines. Additionally, we have a shared mount <code>/scratch/${USER}</code>, which is mounted via NFS from a central file server. This mount point is also mounted on all computing machines. We assume the created and packaged Docker image to be located at <code>/scratch/lukasczy/pynguin.tar</code>. You can change this path by editing the XML files. These XML files contain the basic definitions of the jobs: they specify the SLURM constraint, the version of the Pynguin Docker container, the used Pynguin configurations as well as the modules used for the experiments. These modules have to reside under <code>projects</code>, as they come with this artefact. The Python script <code>execution.py</code> generates the actual run scripts from the XML file. It generates all scripts necessary to run a SLURM array job consisting of all runs for the experiment. Further general settings for the SLURM array job are present in this file. The Bash script <code>run_experiment.sh</code> executes the full execution pipeline; one has to specify the variable <code>EXPERIMENT_NAME</code> to match the name of the respective XML file who's defined experiment shall be executed. Important: Executing the full experiment can take several days, depending on your computing infrastructure! We do therefore provide the raw result CSVs for further inspection. Data Analysis All raw data resides in the <code>data</code> folder: <ul> <li><code>loc_data.csv</code> contains all information about the lines of code in each module. This file was created using the <code>extract_locs_and_types.py</code> script in the root folder. Please note that executing this script requires that the <code>cloc</code> utility tool is installed on your system's path.</li> <li><code>types.csv</code> and <code>types_per_module.csv</code> contain type information extract from the modules at different granularity level. They are also generated using the aforementioned script.</li> <li><code>results-assertion.csv.xz</code> contains the raw results from the experiment for RQ3 that evaluates the effectiveness of the assertions.</li> <li><code>results.csv.xz</code> contains the raw results from the experiment for RQ1 and RQ2.</li> </ul> We provide the Jupyter Notebook that generated the plots, tables, and various LaTeX macros in the <code>notebooks</code> folder. Please note that if you want to reexecute this notebook, you might have to change the <code>PAPER_EXPORT_PATH</code> constant in cell <code>[2]</code> to a suitable location on your machine. Executing this notebook requires a installation of a TeX system to be available on your system because the plots are generated using <code>pdflatex</code> and <code>matplotlib</code>s pgf backend. Further Data The folder <code>projects</code> contains all the projects in the versions stated in our paper. The folder <code>run-logs</code> contains all the run logs from our experiment executions.

opencc-zeroJul 2022View details →

Availability and functionality of neontal care units in health facilities

Background Evidence shows that delivery of prompt and appropriate in-patient newborn care (IPNC) through health facility (HF)-based neonatal care and stabilization units (NCU/NSUs) reduce preventable newborn mortalities (NMs). This study investigated the HFs for the availability and performance of NCU/NSUs in providing quality IPNC, as well as an investigation of influencing factors in Mtwara region, Tanzania. Results About 70.6% (12/17) of surveyed HFs had at least one NCU/NSU room dedicated for delivery of IPNC, and 74.7% (3,600/4,819) of needy newborns were admitted/transferred in for management. Essential medicines such as tetracycline eye ointment was unavailable in 75% (3/4) of the district hospitals (DHs). A disparity existed between the availability and functioning of equipment including infant radiant warmers (92% vs 73%). Governance, support from implementing patterns (IPs), and access to healthcare commodities were identified from qualitative inquiries as factors influencing the establishment and running of NCUs/NSUs at the HFs in Mtwara region, Tanzania. Conclusion Despite the positive progress, the establishment and performance of NCUs/NSUs in providing quality IPNC in HFs in Mtwara has lagged behind the Tanzania neonatal care guideline standards, particularly after the IPs of newborn health interventions completed their terms in 2016. This study suggests additional improvement plans for Mtwara region and other comparable settings to optimize the provision of quality IPNC and lower avoidable NMs.

opencc-by-4.0Jul 2022View details →

Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment

Data used in "Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment".

opencc-by-4.0Sep 2022View details →

Catch-per-unit effort (CPUE) of the West African croaker Pseudotolithus elongatus in the Cross River Estuary, Nigeria

Catch per unit effort, CPUE is a simple and effective statistical method employed by fisheries scientists to describe the catch status of a particular fishery. It can be presented as the total number of fish per hook, or time spent fishing, or trawler, or fisher. It is usually quantified in tons or kg

opencc-zeroSep 2022View details →

Data from: Choice of prioritization method impacts recommendations for climate-informed bird conservation in the United States

Climate-informed spatial planning is urgently needed to guide initiatives aimed at both conserving biodiversity as a whole (e.g., protection of 30% of lands and waters by 2030) and recovering North American avifauna in particular. Various methods for prioritizing conservation areas exist, yet alternative methods may direct managers to different lands for protection and thus varying recommendations for meeting area-based targets. Here, we used bird species distribution models and landcover projections to systematically evaluate two widely-used methods for prioritizing areas most likely to facilitate the persistence of multiple species under climate change: (1) in situ macrorefugia, identified as areas of high predicted species retention; and (2) complementarity-based optimizations, identified using the Zonation conservation planning software. For 17 biogeographical groups in the continental United States, we compared priority areas for bird conservation derived from these two alternatives with respect to their spatial distributions and consensus (i.e., overlap), expected conservation outcomes (e.g., species and functional diversity), predicted climate change exposure, habitat characteristics, landscape configurations, and degree of formal protection. Spatial distributions of priority areas differed by biogeographical group and method, with 40.5% consensus on average across groups. Consensus was extensive within mountainous and coastal regions and limited at high latitudes (e.g., Alaska) and in flat, interior regions (e.g., grasslands). As expected, complementarity-based optimizations more efficiently represented species than retention-based in situ macrorefugia, especially for forest groups, and had greater overall biodiversity value and better habitat condition. Conversely, in situ macrorefugia encompassed higher elevations and larger contiguous patches and were expected to experience less winter-season warming. Formal protection averaged <50% across biogeographical groups, regardless of prioritization method. Our findings illustrate the value of complementarity-based optimizations for bird conservation under climate change. More broadly, comparing approaches for prioritizing areas for long-term species persistence can reveal critical tradeoffs in recommendations for climate-informed protected area planning.