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Scenario A1B emphasizes economic growth with a global orientation and scenario B2 focuses on environmental sustainability with a regional view. Our study area included counties within the southern Great Plains ecoregion in Oklahoma, Texas, and New Mexico. We calculated changes in landscape connectivity (dECA) between 2011 and 2050 for different species groups and landscape scenarios. We also calculated changes in habitat suitability (dA). We assessed the degree to which changes in landscape connectivity were influenced by changes in grassland cover (i.e., the effect of fragmentation per se) by comparing dECA and dA. We also estimated the resistance of the landscape matrix to species movement based on the degree of naturalness of the land-cover type, with more natural land-cover types being less resistant to movement. We calculated the differences between the median value of landscape resistance between 2050 and 2011 for each county to evaluate changes in matrix permeability (i.e., resistance to species movement) for scenario A1B and B2. This data set includes the difference between changes in connectivity (dECA) and changes in grassland cover (dA) for species with a median dispersal distance (MDD) of 1, 5, 10 and 25 km for scenario A1B and scenario B2. For both scenarios results are for counties with net grassland area loss and counties with net grassland area gain. For scenario A1B, 99% of the counties exhibited net grassland area loss, and for scenario B2, 81% of the counties exhibited net grassland area loss."}],"geoLocations":[],"fundingReferences":[{"funderName":"South Central Climate Adaptation Science Center","funderIdentifier":null,"awardTitle":null,"awardNumber":null}],"url":"https://www.sciencebase.gov/catalog/item/5a1dad01e4b09fc93dd7bfb4","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":2,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-09-17T15:43:38Z","registered":"2026-09-17T15:43:38Z","published":null,"updated":"2026-09-17T15:46:43Z"},"relationships":{"client":{"data":{"id":"usgs.prod","type":"clients"}}}},{"id":"10.5066/p13uhs7r","type":"dois","attributes":{"doi":"10.5066/p13uhs7r","identifiers":[],"creators":[{"nameType":"Personal","affiliation":[{"name":"Oklahoma State University"}],"name":"Kristen A Baum","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":null}]},{"nameType":"Personal","affiliation":[{"name":"Oklahoma State University"}],"name":"Elena L Zozaya","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":null}]}],"titles":[{"title":"Dropped-edge analysis of terrestrial connectivity of grassland and forest networks in the South Central United States based on the National Land Cover Database from 2006"}],"publisher":"U.S. Geological Survey","container":{},"publicationYear":2017,"subjects":[],"contributors":[],"dates":[],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementedBy","relatedIdentifier":"https://doi.org/10.5066/P1PCHSON","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"https://doi.org/10.5066/P134QLFC","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This data set includes a dropped-edge analysis of grassland and forest networks in the South Central United States based on land cover data from 2006 and graph theory to evaluate Landscape Resistance to Dispersal (LRD). LRD represents the degree to which habitat availability limits species movement. LRD decreases as habitat availability increases and increases as habitat availability decreases. This data set includes a range of LRD thresholds to represent species with different dispersal abilities and responses to landscape structure. A threshold indicates the highest LRD that still allows dispersal by a particular group of species. LRD thresholds are included in the data set, with low values representing connectivity for species with low movement/dispersal abilities and high values representing connectivity for highly mobile species. Connectivity for highly mobile species includes all of the LRD values below the specified threshold.\n"}],"geoLocations":[],"fundingReferences":[{"funderName":"South Central Climate Adaptation Science Center","funderIdentifier":null,"awardTitle":null,"awardNumber":null}],"url":"https://www.sciencebase.gov/catalog/item/5a1db492e4b09fc93dd7bfe2","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":2,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-09-17T15:40:56Z","registered":"2026-09-17T15:40:56Z","published":null,"updated":"2026-09-17T15:46:16Z"},"relationships":{"client":{"data":{"id":"usgs.prod","type":"clients"}}}},{"id":"10.5281/zenodo.20349999","type":"dois","attributes":{"doi":"10.5281/zenodo.20349999","identifiers":[],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01y8xtk20","name":"Southwestern Oklahoma State University","affiliationIdentifierScheme":"ROR"}],"givenName":"Ronith","familyName":"Sharmila","name":"Sharmila, Ronith","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0009-2633-0511"}]}],"titles":[{"title":"When Attention Repairs Itself: A Mechanistic Study of Backup Heads in GPT-2"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Deep learning","subjectScheme":"EuroSciVoc"},{"subject":"mechanistic interpretability"},{"subject":"hydra effect"},{"subject":"attention circuits"},{"subject":"transformers"},{"subject":"neural networks"},{"subject":"Indirect Object Identification"},{"subject":"TransformerLens"},{"subject":"GPT-2"},{"subject":"Causal Analysis"}],"contributors":[],"dates":[{"date":"2026-05-23","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.20350000","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"A persistent ambiguity in mechanistic interpretability research concerns the causal origin of the Hydra Effect, the observed increase in attention allocated by downstream \"backup\" attention heads to a target token after the ablation of functionally dominant \"primary\" heads. The prevailing hypothesis posits that this spike may be, at least in part, a passive mathematical artifact: removing primary-head contributions collapses signal variance in the residual stream, which triggers LayerNorm scaling changes. These LayerNorm-induced changes can raise a target token’s attention probability when competing logits shift downward or shift less favorably. Disentangling this passive denominator effect from target-directed pre-softmax logit changes has remained methodologically elusive under standard causal tracing.\n\nWe introduce the Frozen Attention Logit Intervention, a surgical counterfactual technique that isolates the dynamic, ablation-induced pre-softmax logit of a single target token while simultaneously freezing all non-target pre-softmax logits to their clean-run baseline values. This construction eliminates the confound of background denominator inflation, yielding a counterfactual attention probability that reflects target-logit attention contributions. We apply this method to GPT-2 Small on a cohort of 200 Indirect Object Identification (IOI) prompts, evaluating all 144 attention heads across 12 layers.\n\nOur principal finding is that in GPT-2 Small on a 200-example mixed-template IOI cohort, ablating L9H9 and L10H0 induces a robust L10H10 increase in IO-directed attention. This effect survives zero, mean, and resample ablation controls. Path-stable FALI/Shapley decomposition shows that the attention increase is dominated by the IO-target attention-logit contribution, while background-logit changes oppose the spike on average. QK decomposition suggests the IO-target score increase is primarily query-driven and largely cosine-alignment-driven. Output mediation shows that the L10H10 attention-pattern change causally contributes to IO-S logit recovery under this intervention, explaining about 43.6% of L10H10’s zero-ablation degradation by aggregate ratio of means. We keep this evidence separate from the LayerNorm-scale freezing diagnostic, which shows that under the Layer-10 clean-scale intervention, 24.3% of the observed L10H10 attention spike is removed (reducing L10H10's attention delta from +0.1893 to +0.1433). Together, these results qualify the role of LayerNorm surges while supporting the presence of target-directed compensatory pathways under upstream failures."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20349999","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2026-05-23T01:11:39Z","registered":"2026-05-23T01:11:39Z","published":null,"updated":"2026-09-15T22:30:44Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20350000","type":"dois","attributes":{"doi":"10.5281/zenodo.20350000","identifiers":[{"identifier":"oai:zenodo.org:20350000","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01y8xtk20","name":"Southwestern Oklahoma State University","affiliationIdentifierScheme":"ROR"}],"givenName":"Ronith","familyName":"Sharmila","name":"Sharmila, Ronith","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0009-2633-0511"}]}],"titles":[{"title":"When Attention Repairs Itself: A Mechanistic Study of Backup Heads in GPT-2"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Deep learning","subjectScheme":"EuroSciVoc"},{"subject":"mechanistic interpretability"},{"subject":"hydra effect"},{"subject":"attention circuits"},{"subject":"transformers"},{"subject":"neural networks"},{"subject":"Indirect Object Identification"},{"subject":"TransformerLens"},{"subject":"GPT-2"},{"subject":"Causal Analysis"}],"contributors":[],"dates":[{"date":"2026-05-23","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20349999","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"A persistent ambiguity in mechanistic interpretability research concerns the causal origin of the Hydra Effect, the observed increase in attention allocated by downstream \"backup\" attention heads to a target token after the ablation of functionally dominant \"primary\" heads. The prevailing hypothesis posits that this spike may be, at least in part, a passive mathematical artifact: removing primary-head contributions collapses signal variance in the residual stream, which triggers LayerNorm scaling changes. These LayerNorm-induced changes can raise a target token’s attention probability when competing logits shift downward or shift less favorably. Disentangling this passive denominator effect from target-directed pre-softmax logit changes has remained methodologically elusive under standard causal tracing.\n\nWe introduce the Frozen Attention Logit Intervention, a surgical counterfactual technique that isolates the dynamic, ablation-induced pre-softmax logit of a single target token while simultaneously freezing all non-target pre-softmax logits to their clean-run baseline values. This construction eliminates the confound of background denominator inflation, yielding a counterfactual attention probability that reflects target-logit attention contributions. We apply this method to GPT-2 Small on a cohort of 200 Indirect Object Identification (IOI) prompts, evaluating all 144 attention heads across 12 layers.\n\nOur principal finding is that in GPT-2 Small on a 200-example mixed-template IOI cohort, ablating L9H9 and L10H0 induces a robust L10H10 increase in IO-directed attention. This effect survives zero, mean, and resample ablation controls. Path-stable FALI/Shapley decomposition shows that the attention increase is dominated by the IO-target attention-logit contribution, while background-logit changes oppose the spike on average. QK decomposition suggests the IO-target score increase is primarily query-driven and largely cosine-alignment-driven. Output mediation shows that the L10H10 attention-pattern change causally contributes to IO-S logit recovery under this intervention, explaining about 43.6% of L10H10’s zero-ablation degradation by aggregate ratio of means. We keep this evidence separate from the LayerNorm-scale freezing diagnostic, which shows that under the Layer-10 clean-scale intervention, 24.3% of the observed L10H10 attention spike is removed (reducing L10H10's attention delta from +0.1893 to +0.1433). Together, these results qualify the role of LayerNorm surges while supporting the presence of target-directed compensatory pathways under upstream failures."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20350000","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-05-23T01:11:39Z","registered":"2026-05-23T01:11:39Z","published":null,"updated":"2026-09-15T22:30:43Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19954174","type":"dois","attributes":{"doi":"10.5281/zenodo.19954174","identifiers":[],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01g9vbr38","name":"Oklahoma State University","affiliationIdentifierScheme":"ROR"}],"givenName":"Neelansh","familyName":"Neelansh","name":"Neelansh, Neelansh","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-3126-9083"}]}],"titles":[{"title":"Discrepancy between Conceptual understanding and Academic performance"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Education"},{"subject":"psychology"},{"subject":"cognitive learning"}],"contributors":[],"dates":[{"date":"2026-05-01","dateType":"Issued"},{"date":"2026-05-01","dateType":"Submitted"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19954175","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v1.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"},{"rightsUri":"http://rightsstatements.org/vocab/InC/1.0/","rights":"© 2026 Neelansh Srivastava. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), permitting use, distribution, and reproduction in any medium, provided the original author is credited."}],"descriptions":[{"descriptionType":"Abstract","description":"This study investigates the relationship between conceptual understanding and academic performance among secondary school students. Using survey data from approximately 65students in Grades 9–12, the analysis examines study strategies, self-reported understanding, and examination outcomes. Statistical results indicate a weak positive correlation (r ≈ 0.32), suggesting that while conceptual clarity contributes to performance, it does not strongly predict it. A notable proportion of students report achieving high scores through memorization or mixed strategies, highlighting a gap between learning and evaluation. The findings suggest that conventional assessments may not fully capture deeper understanding and point toward the need for evaluation methods that emphasize application and reasoning."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19954174","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2026-05-10T08:51:34Z","registered":"2026-05-10T08:51:34Z","published":null,"updated":"2026-09-15T17:07:13Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19954175","type":"dois","attributes":{"doi":"10.5281/zenodo.19954175","identifiers":[{"identifier":"oai:zenodo.org:19954175","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01g9vbr38","name":"Oklahoma State University","affiliationIdentifierScheme":"ROR"}],"givenName":"Neelansh","familyName":"Neelansh","name":"Neelansh, Neelansh","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-3126-9083"}]}],"titles":[{"title":"Discrepancy between Conceptual understanding and Academic performance"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Education"},{"subject":"psychology"},{"subject":"cognitive learning"}],"contributors":[],"dates":[{"date":"2026-05-01","dateType":"Issued"},{"date":"2026-05-01","dateType":"Submitted"}],"language":"en","types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19954174","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v1.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"},{"rightsUri":"http://rightsstatements.org/vocab/InC/1.0/","rights":"© 2026 Neelansh Srivastava. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), permitting use, distribution, and reproduction in any medium, provided the original author is credited."}],"descriptions":[{"descriptionType":"Abstract","description":"This study investigates the relationship between conceptual understanding and academic performance among secondary school students. Using survey data from approximately 65students in Grades 9–12, the analysis examines study strategies, self-reported understanding, and examination outcomes. Statistical results indicate a weak positive correlation (r ≈ 0.32), suggesting that while conceptual clarity contributes to performance, it does not strongly predict it. A notable proportion of students report achieving high scores through memorization or mixed strategies, highlighting a gap between learning and evaluation. The findings suggest that conventional assessments may not fully capture deeper understanding and point toward the need for evaluation methods that emphasize application and reasoning."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19954175","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-05-10T08:51:34Z","registered":"2026-05-10T08:51:34Z","published":null,"updated":"2026-09-15T17:07:12Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20418171","type":"dois","attributes":{"doi":"10.5281/zenodo.20418171","identifiers":[],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01g9vbr38","name":"Oklahoma State University","affiliationIdentifierScheme":"ROR"}],"givenName":"Sam","familyName":"Miess","name":"Miess, Sam","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0008-8240-918X"}]}],"titles":[{"title":"Scripts and datasets associated with \"Incorporating community assembly mechanisms in biotic indices improves index performance\" in Ecological Applications"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-05-27","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"ScholarlyArticle","resourceTypeGeneral":"JournalArticle","citeproc":"article-journal","bibtex":"article","ris":"JOUR","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.20418172","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"README\n\nThe following information serves as the metadata for the files associated with “Incorporating community assembly mechanisms in biotic indices improves index performance” published in Ecological Applications. All analyses can be run in Rstudio with the “EcoApp2025pubCode.R” and “EcoApp25.R” files.\n\nMetadata for Data.for.EcoApp25.xlsx\n\nOverview: Workbook contains five sheets: `Build`, `testdata`, `AllMech`, `Family2`, and `Genus2`. Blank cells indicate missing, unavailable, or not-applicable values unless otherwise specified. Numeric zeros in taxon-count columns indicate that zero individuals of that taxon were recorded for that sample. Taxonomic columns may include genera, families, tribes, or other operational taxonomic units, depending on the taxonomic resolution available in the source data.\n\n1.       Sheet: Build\n\nDescription: Site-level stream macroinvertebrate, water-quality, habitat, and taxon-abundance data from the Oklahoma Conservation Commission’s Rotating Basin Monitoring Program. Each row represents one biological sample or site-by-index-period sample. Early columns describe sampling location, date/season, water chemistry, and habitat metrics. Remaining columns are taxon abundance/count columns.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| NumOrder | numeric/integer | Row or sample ordering number used in the compiled dataset. |\n\n| SiteName | categorical/text | Name of the stream or sampling site. |\n\n| WBID | categorical/text | Site identifier code |\n\n| SAMPLE | categorical/text | Composite sample identifier, generally combining WBID, index period, and year. |\n\n| Basin | categorical/text | Major drainage basin containing the sampling site. |\n\n| SAMPLE.1 | categorical/text | Duplicate or alternate sample identifier|\n\n| L3 Ecoregion | categorical/text | Level III ecoregion name. |\n\n| Latitude | numeric | Site latitude in decimal degrees. |\n\n| Longitude | numeric | Site longitude in decimal degrees. |\n\n| LegalDesc | categorical/text | Legal land description of the sampling location, where available. |\n\n| County | categorical/text | County containing the sampling site. |\n\n| Ecoregion | categorical/text | Ecoregion name or abbreviation used in the compiled dataset. Values include full ecoregion names and abbreviated codes. |\n\n| Modified Ecoregion | categorical/text | Modified ecoregion classification/code where available. Example codes include `CGP`, `SWT`, `FH`, `CIP`, `CT`, `OH`, `AV`, `BM`, `OM`, `SCP`, and `ECTP`. |\n\n| Date | date | Sample collection date. |\n\n| Index | categorical/text | Seasonal index period for the sample, e.g., `Summer` or `Winter`. |\n\n| Year | numeric/integer | Calendar year associated with the sample. |\n\n| IndexYear | categorical/text | Combined index period and year, e.g., `Summer2001`. |\n\n| DO (mg/L) | numeric | Dissolved oxygen concentration, in milligrams per liter. |\n\n| Temp (°C) | numeric | Water temperature, in degrees Celsius. |\n\n| Turbidity (NTU) | numeric | Turbidity, in nephelometric turbidity units. |\n\n| Alkalinity (CaCO3) | numeric | Alkalinity reported as calcium carbonate. Units presumed mg/L as CaCO3. |\n\n| Conductivity | numeric | Specific conductance/conductivity in µS/cm. |\n\n| pH (SU) | numeric | pH in standard units. |\n\n| Flow | numeric/categorical | Field flow measurement or flow condition in cubic feet per second|\n\n| BaseFlow | numeric/categorical | Baseflow measurement or flow condition in cubic feet per second|\n\n| DOPercSat | numeric | Dissolved oxygen percent saturation. |\n\n| CBOD (mg/L) | numeric | Carbonaceous biochemical oxygen demand, in milligrams per liter. |\n\n| Chloride (mg/L) | numeric | Chloride concentration, in milligrams per liter. |\n\n| Sulfate (mg/L) | numeric | Sulfate concentration, in milligrams per liter. |\n\n| Ammonia (mg/L) | numeric | Ammonia concentration, in milligrams per liter. |\n\n| Nitrate (mg/L) | numeric | Nitrate concentration, in milligrams per liter. |\n\n| Nitrite (mg/L) | numeric | Nitrite concentration, in milligrams per liter. |\n\n| TKN (mg/L) | numeric | Total Kjeldahl nitrogen, in milligrams per liter. |\n\n| TotOrthoPhos (mg/L) | numeric | Total orthophosphate concentration, in milligrams per liter. |\n\n| Total Phosphorus (mg/L) | numeric | Total phosphorus concentration, in milligrams per liter. |\n\n| TSS (mg/L) | numeric | Total suspended solids, in milligrams per liter. |\n\n| TotDisSolids (mg/L) | numeric | Total dissolved solids, in milligrams per liter. |\n\n| Hardness (mg/L) | numeric | Water hardness, in milligrams per liter, likely as CaCO3. |\n\n| Enterococcus | numeric | Enterococcus bacteria measurement. Units should be confirmed from source documentation, commonly colony-forming units or MPN per 100 mL. |\n\n| E.Coli | numeric | Escherichia coli measurement. Units should be confirmed from source documentation, commonly colony-forming units or MPN per 100 mL. |\n\n| Instream Cover | numeric | Habitat score for instream cover. Higher values indicate greater habitat condition/availability according to the source scoring system. |\n\n| Pool Bottom Substrate | numeric | Habitat score for pool-bottom substrate condition. |\n\n| Pool Variability | numeric | Habitat score for pool variability. |\n\n| Canopy Cover Shading | numeric | Habitat score for canopy cover or shading. |\n\n| Presence of Rocky Runs or Riffles | numeric | Habitat score representing presence/quality of rocky runs or riffles. |\n\n| FlowHab | numeric | Habitat score related to flow conditions or flow habitat. |\n\n| Channel Alteration | numeric | Habitat score for degree of channel alteration. |\n\n| Channel Sinuosity | numeric | Habitat score for channel sinuosity. |\n\n| Bank Stability | numeric | Habitat score for bank stability. |\n\n| Bank Vegetation Stability | numeric | Habitat score for bank vegetation stability. |\n\n| Streamside Cover | numeric | Habitat score for riparian or streamside cover. |\n\n| Total Points | numeric | Total habitat score, calculated from component habitat metrics. |\n\n| Stenelmis through Lepidostomatidae | numeric/integer | All remaining columns are named for macroinvertebrate taxa or operational taxonomic units and represent the number of individuals recorded for that taxon in the sample. Taxonomic resolution varies among columns and may include genus, family, tribe, or ambiguous names. Examples include `Stenelmis`, `Chironomini`, `Orthocladiinae`, `Baetis`, `Hydropsyche`, and `Lepidostomatidae`. Blank cells indicate missing or unavailable taxon-count information; zeros indicate that no individuals of that taxon were recorded for that sample. |\n\n2.       Sheet: testdata\n\nDescription: Long-format modeling or analysis dataset containing site/sample identifiers, ecoregion, disturbance classification, taxonomic identity, abundance, habitat, water-quality variables, and biological index values as compiled from the Environmental Protection Agency’s National Rivers and Streams Assessment.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| UID | categorical/text| Unique site or sample identifier. |\n\n| Ecoregion | categorical/text | Ecoregion associated with the sample. Values include ecoregion names; capitalization is not fully standardized. |\n\n| DispersalCut | numeric | Dispersal-threshold or cutoff value used in classification/modeling for previous integration approach. This is no longer used in the final methodology|\n\n| NicheCut | numeric/integer | Niche-breadth cutoff value used in classification/modeling for previous integration approach. This is no longer used in the final methodology|\n\n| CooccurCut | numeric/integer | Co-occurrence/interactions cutoff value used in classification/modeling for previous integration approach. This is no longer used in the final methodology. |\n\n| Year | numeric/integer | Year of sample collection. |\n\n| Month | numeric/integer | Month of sample collection, coded 1–12. |\n\n| Classification | categorical/text | Disturbance classification assigned to the sample by the EPA. Observed values include `Least Disturbed`, `Intermediate Disturbance`, `Most Disturbed`, and `?`. The value `?` indicates unknown or unresolved classification. |\n\n| Family | categorical/text | Macroinvertebrate family name, when available. |\n\n| Species | categorical/text | Taxon name used in the analysis. Despite the column name, values may represent genus, family, tribe, or other taxonomic levels depending on identification resolution. The naming convention “species” was used as it helped expediate table joins; it in no way indicates the true species name.|\n\n| Count | numeric | Number of individuals recorded for the taxon in the sample. |\n\n| Habitat | numeric | Habitat score or habitat metric associated with the sample. Higher/lower interpretation depends on the source scoring system. |\n\n| DO | numeric | Dissolved oxygen concentration in mg/L|\n\n| Temp | numeric | Water temperature, in degrees Celsius. |\n\n| pH | numeric | pH in standard units. |\n\n| Cond | numeric | Conductivity/specific conductance in µS/cm. |\n\n| Turb | numeric | Turbidity in NTU. |\n\n| TP | numeric | Total phosphorus concentration in µg/L |\n\n| NH4 | numeric | Ammonium/ammonia nitrogen measurement in mg/L |\n\n| NO3 | numeric | Nitrate measurement in mg/L |\n\n| TN | numeric | Total nitrogen measurement in mg/L |\n\n| PTV | numeric | Pollution tolerance value or biological tolerance value associated with the taxon.|\n\n \n\n3.       Sheet: AllMech\n\nDescription: Taxon-level trait/mechanism table describing taxonomic identity, occurrence, interaction counts (from previous interaction metric), niche breadth, dispersal score, and assigned dispersal type.\n\n \n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Taxa | categorical/text | Taxon name. Values may represent genus, family, or another operational taxonomic unit. |\n\n| Family | categorical/text | Taxonomic family associated with “Taxa”, where available. |\n\n| Order | categorical/text | Taxonomic order associated with “Taxa”, where available. |\n\n| Sites | numeric/integer | Number of sites where the taxon occurred or was included. In this sheet, values appear to be 1 for all rows. |\n\n| Interactions | numeric/integer | Number of co-occurrence interactions, as determined via Veech’s Probabilistic model for co-occurrence. These values were calculated during a previous analytical approach but are not useful for the current manuscript.|\n\n| NicheBreadth | numeric | Estimated niche breadth value for the taxon. Higher values indicate broader inferred environmental or ecological niche breadth, as calculated based on weighted standard deviations|\n\n| SFP | numeric | Species flight propensity, the metric used for approximating dispersal capacity. |\n\n| DispersalType | categorical/text | Assigned dispersal type. Observed values are `Aerial` and `Aquatic`. |\n\n4.       Sheet: Family2\n\nDescription: Family-level optima by ecoregion. Each row represents one family-level taxon within an ecoregion. Numeric values represent estimated environmental optima, based on the given abiotic variable. Some of these values were log-transformed before optima were calculated; as such, some values appear negative. Family-level values were used when genus-level optima were not available/collected.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Ecoregion | categorical/text | Ecoregion code for the estimate. Observed codes include `All`, `AV`, `BM`, `CGP`, `CIP`, `CT`, `OH`, `OM`, `SCP`, and `SWT`. |\n\n| Species | categorical/text | Taxon name. In this sheet, values appear primarily to be family-level names despite the column name `Species`. |\n\n| Do | numeric | Estimated optimum for dissolved oxygen. Note capitalization differs from `DO` elsewhere. |\n\n| Temp | numeric | Estimated optimum for water temperature. |\n\n| Turbidity | numeric | Estimated optimum for turbidity. |\n\n| NH4 | numeric | Estimated optimum for ammonium/ammonia. |\n\n| Conductivity | numeric | Estimated optimum for conductivity. |\n\n| pH | numeric | Estimated optimum for pH. |\n\n| Ammonia | numeric | Estimated optimum for ammonia. |\n\n| Nitrate | numeric | Estimated optimum for nitrate. |\n\n| Nitrite | numeric | Estimated optimum for nitrite. |\n\n| TKN | numeric | Estimated optimum for total Kjeldahl nitrogen. |\n\n| TP | numeric | Estimated optimum for total phosphorus. |\n\n| E.coli | numeric | Estimated optimum for Escherichia coli. |\n\n| Instream | numeric | Estimated optimum for instream habitat condition. |\n\n| Habitat | numeric | Estimated optimum for overall habitat condition. |\n\n \n\nb.       Ecoregion code definitions\n\n| Code | Ecoregion |\n\n| All | All ecoregions combined |\n\n| AV | Arkansas Valley |\n\n| BM | Boston Mountains |\n\n| CGP | Central Great Plains |\n\n| CIP | Central Irregular Plains |\n\n| CT | Cross Timbers |\n\n| OH | Ozark Highlands |\n\n| OM | Ouachita Mountains |\n\n| SCP | South Central Plains |\n\n| SWT | Southwestern Tablelands |\n\n \n\n5.       Sheet: Genus2\n\nDescription: Genus-level optima by ecoregion. Each row represents one family-level taxon within an ecoregion. Numeric values represent estimated environmental optima, based on the given abiotic variable. Some of these values were log-transformed before optima were calculated; as such, some values appear negative. Family-level values were used when genus-level optima were not available/collected.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Species | categorical/text | Taxon name. Despite the column name `Species`, values may represent genus, family, or another operational taxonomic unit depending on taxonomic resolution. |\n\n| Do | numeric | Estimated optimum for dissolved oxygen. Note capitalization differs from `DO` elsewhere. |\n\n| Temp | numeric | Estimated optimum for water temperature. |\n\n| NH4 | numeric | Estimated optimum for ammonium/ammonia. |\n\n| Turbidity | numeric | Estimated optimum for turbidity. |\n\n| Conductivity | numeric | Estimated optimum for conductivity. |\n\n| pH | numeric | Estimated association optimum for pH. |\n\n| Ammonia | numeric | Estimated optimum for ammonia. |\n\n| Nitrate | numeric | Estimated optimum for nitrate. |\n\n| Nitrite | numeric | Estimated ptimum for nitrite. |\n\n| TKN | numeric | Estimated optimum for total Kjeldahl nitrogen. |\n\n| TP | numeric | Estimated optimum for total phosphorus. |\n\n| E.coli | numeric | Estimated optimum for Escherichia coli. |\n\n| Instream | numeric | Estimated optimum for instream habitat condition. |\n\n| Habitat | numeric | Estimated optimum for overall habitat condition. |\n\n| Ecoregion | categorical/text | Ecoregion code for the estimate. Observed codes include `All`, `AV`, `BM`, `CGP`, `CIP`, `CT`, `OH`, `OM`, `SCP`, and `SWT`. |\n\nb.       Ecoregion code definitions\n\n| Code | Ecoregion |\n\n| All | All ecoregions combined |\n\n| AV | Arkansas Valley |\n\n| BM | Boston Mountains |\n\n| CGP | Central Great Plains |\n\n| CIP | Central Irregular Plains |\n\n| CT | Cross Timbers |\n\n| OH | Ozark Highlands |\n\n| OM | Ouachita Mountains |\n\n| SCP | South Central Plains |\n\n| SWT | Southwestern Tablelands |\n\n \n\n6.       Missing values and special codes\n\n·       Blank cells indicate missing, unavailable, or not-applicable data unless otherwise stated.\n\n·       In taxon-abundance columns, numeric `0` indicates no individuals of that taxon were recorded for the sample.\n\n·       The value `?` in `testdata$Classification` indicates unresolved or unknown disturbance classification.\n\n·       Taxonomic columns may include multiple taxonomic resolutions. The column name `Species` does not always indicate species-level identification.\n\n·       Ecoregion names/codes are not fully standardized across sheets; some sheets use full names, while others use abbreviations.\n\n \n\n7.       Citation information\n\nThe “Build” sheet contains data compiled from the Oklahoma Conservation Commission’s Rotating Basin Monitoring Program. The reports from which the data were extracted can be found here:\n\n \n\nWQ-Statewide Rotating Basin Monitoring Program - Oklahoma Conservation\n\nCommission. 2021, July 2.https://conservation.ok.gov/wq-statewide-rotating-basin-monitoring-program/\n\n \n\nSpecifics regarding how each variable was measured or otherwise collected can be found in their standard operating procedures here:\n\n \n\nOklahoma Conservation Commission. 2023. Standard operating procedures for\n\nwater quality monitoring and measurement activities. Oklahoma Conservation Commission Water Quality Division. https://conservation.ok.gov/wp-content/uploads/2023/08/sop.pdf\n\n \n\nThe “testdata” sheet contains data compiled from the Environmental Protection Agency’s National Rivers and Streams Assessment. The datasets from which data were pulled can be found here:\n\n \n\nUS EPA. 2015, June 23. National Rivers and Streams Assessment. Overviews and\n\nFactsheets. https://www.epa.gov/national-aquatic-resource-surveys/nrsa.\n\n \n\nSpecifics regarding how each variable was measured or otherwise collected by US EPA can be found in their standard operating procedures here:\n\n \n\nUSEPA. 2022. National Rivers and Streams Assessment 2023/24: Field Operations\n\nManual – Wadeable. U.S. Environmental Protection Agency, Office of Water, Washington, DC. https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-streams-assessment-2023-24-field-operations\n\nThe Species Flight Propensity (SFP) index, which was used for quantifying dispersal capacity, was calculated based on the scoring proposed by Sarremejane et al (2017). This index can be found here:\n\nSarremejane, R., H. Mykrä, N. Bonada, J. Aroviita, and T. Muotka. 2017. Habitat\n\nconnectivity and dispersal ability drive the assembly mechanisms of macroinvertebrate communities in river networks. Freshwater Biology 62:1073–1082.\n\n \n\n \n\nMetadata for EcoApp25SBIData.xlsx\n\nThis .xlsx contains six sheets: `OCC`, `Variables`, `EPA`, `SBItest`, `Groups_Mechanisms`, and `SBI`. Blank cells indicate missing, unavailable, or not-applicable values unless otherwise specified. In wide-format taxon-abundance columns, blank cells indicate that the taxon was not recorded in that sample and should be interpreted as zero abundance for community-composition analyses, unless otherwise noted in the analysis code.\n\n1.       Sheet: OCC\n\nDescription: Wide-format stream macroinvertebrate, water-quality, habitat, and site-level dataset from the Oklahoma Conservation Commission’s Rotating Basin Monitoring Program. Each row represents one stream sample. Early columns describe sample identity, geographic location, region/ecoregion, collection timing, water chemistry, and habitat metrics. All columns beginning with `Stenelmis` and continuing through `Lepidostomatidae` are taxon-abundance columns.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| NumOrder | numeric/integer | Row-ordering number used in the compiled dataset. |\n\n| SiteName | categorical/text | Stream or sampling-site name. |\n\n| WBID | categorical/text | Site identifier. |\n\n| SAMPLE | categorical/text | Composite sample identifier, generally combining WBID, index period, and year. |\n\n| Basin | categorical/text | Major drainage basin associated with the sample. |\n\n| SAMPLE2 | categorical/text | Alternate or duplicate composite sample identifier. |\n\n| L3 Ecoregion | categorical/text | Level III ecoregion name. |\n\n| Region | categorical/text | Regional classification used for analysis. Values include named regions such as `Ozark Highlands`, `Boston Mountains`, `Central Great Plains`, and others. |\n\n| Aggregate | categorical/text | Aggregated regional/ecological grouping used for analysis. Observed codes include `SAP`, `NAP`, `TPL`, `SPL`, `UMW`, `WMT`, `NPL`, and `XER`. |\n\n| DispersalCut | numeric | Dispersal-score cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| NicheCut | numeric | Niche-breadth cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| CooccurCut | numeric | Co-occurrence/interactions cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| Latitude | numeric | Sampling-site latitude in decimal degrees. |\n\n| Longitude | numeric | Sampling-site longitude in decimal degrees. |\n\n| LegalDesc | categorical/text | Legal land description of the sampling location, where available. |\n\n| County | categorical/text | County containing the sampling site. |\n\n| Ecoregion | categorical/text | Ecoregion classification used in the dataset. |\n\n| Date | date | Sample collection date. |\n\n| Index | categorical/text | Seasonal index period for the sample, e.g., `Summer` or `Winter`. |\n\n| Year | numeric/integer | Calendar year of sample collection. |\n\n| IndexYear | categorical/text | Combined index period and year, e.g., `Summer2001`. |\n\n| DO | numeric | Dissolved oxygen concentration. Units are presumed mg/L. |\n\n| Temp | numeric | Water temperature, in degrees Celsius. |\n\n| Turb | numeric | Turbidity. Units are NTU. |\n\n| Cond | numeric | Conductivity or specific conductance in µS/cm. |\n\n| pH | numeric | pH in standard units. |\n\n| Habitat | numeric | Overall habitat score or habitat-condition metric. Higher/lower interpretation follows the source scoring protocol. |\n\n| TP | numeric | Total phosphorus concentration, units are in ug/L|\n\n| TN | numeric | Total nitrogen concentration, units in mg/L|\n\n| NH4 | numeric | Ammonium/ammonia measurement, units in mg/L|\n\n| NO3 | numeric | Nitrate measurement, units in mg/L|\n\n| NO2 | numeric | Nitrite measurement, units in mg/L|\n\n| Flow | numeric/categorical | Field flow measurement or flow condition, units in cubic feet per second|\n\n| Sulfate (mg/L) | numeric | Sulfate concentration, in milligrams per liter. |\n\n| TotOrthoPhos (mg/L) | numeric | Total orthophosphate concentration, in milligrams per liter. |\n\n| TSS (mg/L) | numeric | Total suspended solids, in milligrams per liter. |\n\n| TotDisSolids (mg/L) | numeric | Total dissolved solids, in milligrams per liter. |\n\n| Hardness (mg/L) | numeric | Water hardness, in milligrams per liter, likely as CaCO3. |\n\n| Enterococcus | numeric | Enterococcus bacteria measurement. Units should be verified from source documentation, commonly CFU or MPN per 100 mL. |\n\n| E.Coli | numeric | Escherichia coli bacteria measurement. Units should be verified from source documentation, commonly CFU or MPN per 100 mL. |\n\n| Instream Cover | numeric | Habitat score for instream cover. |\n\n| Pool Bottom Substrate | numeric | Habitat score for pool-bottom substrate condition. |\n\n| Pool Variability | numeric | Habitat score for pool variability. |\n\n| Canopy Cover Shading | numeric | Habitat score for canopy cover or shading. |\n\n| Presence of Rocky Runs or Riffles | numeric | Habitat score representing presence or quality of rocky runs/riffles. |\n\n| FlowHab | numeric | Habitat score related to flow habitat. |\n\n| Channel Alteration | numeric | Habitat score for degree of channel alteration. |\n\n| Channel Sinuosity | numeric | Habitat score for channel sinuosity. |\n\n| Bank Stability | numeric | Habitat score for streambank stability. |\n\n| Bank Vegetation Stability | numeric | Habitat score for bank vegetation stability. |\n\n| Streamside Cover | numeric | Habitat score for riparian or streamside cover. |\n\n| Stenelmis through Lepidostomatidae | numeric/integer | All remaining columns are named for macroinvertebrate taxa or operational taxonomic units and represent the abundance/count of individuals recorded for that taxon in the sample. Taxonomic resolution varies among columns and may include genus, family, subfamily, tribe, or ambiguous operational names. Blank cells indicate that the taxon was not recorded in that sample and should be interpreted as zero abundance for community-composition analyses. |\n\n2.       Sheet: Variables\n\nDescription: Lookup sheet identifying variables used in the compiled analysis dataset and taxa included in the macroinvertebrate abundance matrix.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Columns | categorical/text | Names of selected analytical variables used elsewhere in the workbook, including sample identifiers, regional classifications, water-quality variables, habitat variables, taxon names, and count fields. |\n\n| Bugs | categorical/text | Macroinvertebrate taxon names included in the abundance matrix. Taxa may be identified to genus, family, subfamily, tribe, or another operational taxonomic unit depending on identification resolution. |\n\n \n\n3.       Sheet: EPA\n\nDescription: Long-format macroinvertebrate dataset compiled from the Environmental Protection Agency’s National Rivers and Streams Assessment. Each row represents the abundance/count of one taxon in one stream sample, with associated site/sample metadata, region/ecoregion, water-quality variables, habitat score, chloride, and tolerance-value information.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| SAMPLE | categorical/text | Sample identifier. |\n\n| Ecoregion | categorical/text | Ecoregion name associated with the sample. Values are generally uppercase names. |\n\n| Region | categorical/text | Regional classification used for analysis, e.g., `Boston Mountains`, `Ozark Highlands`, `Central Irregular Plains`, and others. |\n\n| Aggregate | categorical/text | Aggregated regional/ecological grouping used for analysis. Observed codes include `SAP`, `NAP`, `TPL`, `SPL`, `UMW`, `WMT`, `NPL`, and `XER`. |\n\n| DispersalCut | numeric | Dispersal-score cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| NicheCut | numeric | Niche-breadth cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| CooccurCut | numeric | Co-occurrence/interactions cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| Year | numeric/integer | Calendar year of sample collection. |\n\n| Month | numeric/integer | Month of sample collection, coded 1–12. |\n\n| Classification | categorical/text | Disturbance or condition classification assigned to the sample, where available. Blank cells indicate no classification was assigned or available. |\n\n| Family | categorical/text | Taxonomic family assigned to the organism. |\n\n| Taxa | categorical/text | Taxon name used in analysis. Values may represent genus, family, or another operational taxonomic unit depending on taxonomic resolution. |\n\n| Count | numeric/integer | Number of individuals of the taxon recorded in the sample. |\n\n| Habitat | numeric | Habitat score or habitat metric associated with the sample. |\n\n| DO | numeric | Dissolved oxygen concentration in  mg/L. |\n\n| Temp | numeric | Water temperature, in degrees Celsius. |\n\n| pH | numeric | pH in standard units. |\n\n| Cond | numeric | Conductivity or specific conductance in µS/cm. |\n\n| Turb | numeric | Turbidity in NTU. |\n\n| TP | numeric | Total phosphorus concentration in micrograms per liter|\n\n| NH4 | numeric | Ammonium/ammonia measurement in mg/L|\n\n| NO3 | numeric | Nitrate measurement in mg/L|\n\n| TN | numeric | Total nitrogen concentration in mg/L|\n\n| Chloride | numeric | Chloride concentration in mg/L. |\n\n| PTV | numeric | Pollution tolerance value assigned to the taxon. Higher values generally indicate greater tolerance to pollution or environmental stress, as determined by the EPA|\n\n \n\n4.       Sheet: SBItest\n\nDescription: Long-format dataset used for testing the  Salt Belt Index/biotic index. Each row represents a taxon occurrence/count in a sample, paired with chloride concentration and regional grouping.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| UID | categorical/text or numeric | Unique sample or site identifier. Treat as an identifier, not as a continuous numeric variable. |\n\n| Region | categorical/text | Aggregated regional grouping used for analysis. Observed codes include values such as `SAP` and `NAP`. |\n\n| Chloride | numeric | Chloride concentration associated with the sample in mg/L. |\n\n| Family2 | categorical/text | Harmonized or analysis-ready family name for the taxon. |\n\n| Taxa.org | categorical/text | Original taxon name or original taxonomic label before harmonization. |\n\n| Taxa | categorical/text | Harmonized taxon name used in analysis. |\n\n| Count | numeric/integer | Number of individuals of the taxon recorded in the sample. |\n\n \n\n5.       Sheet: Groups_Mechanisms\n\nDescription: Taxon-level trait/mechanism table describing taxonomic identity, occurrence, interaction counts, niche breadth, dispersal score, and assigned dispersal mode.\n\na.        Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Taxa | categorical/text | Taxon name. Values may represent genus, family, or another operational taxonomic unit. |\n\n| Family | categorical/text | Taxonomic family associated with `Taxa`, where available. |\n\n| Order | categorical/text | Taxonomic order associated with `Taxa`, where available. |\n\n| Sites | numeric/integer | Number of sites or site groups associated with the taxon in this table. |\n\n| Interactions | numeric/integer | Number of co-occurrences as determined using Veech’s Probabilistic Model for Cooccurrence. These were calculated for an earlier analytical approach but are not used for the current workflow.|\n\n| NicheBreadth | numeric | Estimated niche-breadth value for the taxon, based on weighted standard deviations. Higher values indicate broader inferred environmental or ecological niche breadth. |\n\n| SFP | numeric | Species Flight Propensity Index value used as a metric for dispersal capacity. |\n\n| DispersalType | categorical/text | Assigned dispersal type. Observed values include `Aerial` and `Aquatic`. |\n\n \n\n6.       Sheet: SBI\n\nDescription: Taxon-level Salt Belt Index or stressor-tolerance table. Each row represents a macroinvertebrate taxon and its assigned index/tolerance values by aggregated regional group. Regional columns contain numeric taxon scores for each region where a value was available.\n\na.        Column-level metadata\n\n \n\n| Column name | Data type | Description / interpretation |\n\n| Phylum | categorical/text | Taxonomic phylum. |\n\n| Class | categorical/text | Taxonomic class. |\n\n| Order | categorical/text | Taxonomic order. |\n\n| Family | categorical/text | Taxonomic family. |\n\n| Taxa.original | categorical/text | Original taxon label before harmonization. |\n\n| Taxa | categorical/text | Harmonized taxon name used in analysis. |\n\n| SAP | numeric | Taxon SBI/tolerance value for the SAP regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| NAP | numeric | Taxon SBI/tolerance value for the NAP regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| UMW | numeric | Taxon SBI/tolerance value for the UMW regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| TPL | numeric | Taxon SBI/tolerance value for the TPL regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| SPL | numeric | Taxon SBI/tolerance value for the SPL regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| XER | numeric | Taxon SBI/tolerance value for the XER regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| WMT | numeric | Taxon SBI/tolerance value for the WMT regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| NPL | numeric | Taxon SBI/tolerance value for the NPL regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n \n\n7.       Regional code notes\n\n| Code | Description |\n\n| Code | Ecoregion |\n\n| All | All ecoregions combined |\n\n| AV | Arkansas Valley |\n\n| BM | Boston Mountains |\n\n| CGP | Central Great Plains |\n\n| CIP | Central Irregular Plains |\n\n| CT | Cross Timbers |\n\n| OH | Ozark Highlands |\n\n| OM | Ouachita Mountains |\n\n| SCP | South Central Plains |\n\n| SWT | Southwestern Tablelands |\n\n \n\n8.       Missing values and special coding\n\n·       Blank cells indicate missing, unavailable, or not-applicable data unless otherwise stated.\n\n·       In `OCC`, taxon columns from `Stenelmis` through `Lepidostomatidae` are wide-format abundance/count columns. Blank cells indicate that the taxon was not recorded in that sample and should be treated as zero abundance for community analyses.\n\n·       In `SBI`, blank regional score cells indicate no SBI/tolerance value was assigned or available for that taxon-region combination.\n\n·        Taxonomic columns may include mixed taxonomic resolution. Names may represent genus, family, subfamily, tribe, or another operational taxonomic unit.\n\n·       The column name `Taxa` is used broadly and does not always imply species-level resolution.\n\n·       `SFP`, `DispersalCut`, `NicheCut`, and `CooccurCut` should be defined in the README using the exact terminology from the manuscript or analysis code.\n\n \n\n9.       Citation information\n\nThe “OCC” sheet contains data compiled from the Oklahoma Conservation Commission’s Rotating Basin Monitoring Program. The reports from which the data were extracted can be found here:\n\n \n\nWQ-Statewide Rotating Basin Monitoring Program - Oklahoma Conservation\n\nCommission. 2021, July 2.https://conservation.ok.gov/wq-statewide-rotating-basin-monitoring-program/\n\n \n\nSpecifics regarding how each variable was measured or otherwise collected can be found in their standard operating procedures here:\n\n \n\nOklahoma Conservation Commission. 2023. Standard operating procedures for\n\nwater quality monitoring and measurement activities. Oklahoma Conservation Commission Water Quality Division. https://conservation.ok.gov/wp-content/uploads/2023/08/sop.pdf\n\n \n\nThe “EPA” sheet contains data compiled from the Environmental Protection Agency’s National Rivers and Streams Assessment. The datasets from which data were pulled can be found here:\n\n \n\nUS EPA. 2015, June 23. National Rivers and Streams Assessment. Overviews and\n\nFactsheets. https://www.epa.gov/national-aquatic-resource-surveys/nrsa.\n\n \n\nSpecifics regarding how each variable was measured or otherwise collected by US EPA can be found in their standard operating procedures here:\n\n \n\nUSEPA. 2022. National Rivers and Streams Assessment 2023/24: Field Operations\n\nManual – Wadeable. U.S. Environmental Protection Agency, Office of Water, Washington, DC. https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-streams-assessment-2023-24-field-operations\n\nThe Species Flight Propensity (SFP) index, which was used for quantifying dispersal capacity, was calculated based on the scoring proposed by Sarremejane et al (2017). This index can be found here:\n\nSarremejane, R., H. Mykrä, N. Bonada, J. Aroviita, and T. Muotka. 2017. Habitat\n\nconnectivity and dispersal ability drive the assembly mechanisms of macroinvertebrate communities in river networks. Freshwater Biology 62:1073–1082.\n\nThe “SBI” sheet contains scores and values from Miess and Dzialowski (2024), and information regarding these data can be found here:\n\nMiess, S., and A. R. Dzialowski. 2024. Salt Belt Index (SBI): A biotic index for streams\n\nwithin the North American “salt belt,” with proposed baseline chloride thresholds. Science of The Total Environment 941:173726.\n\n \n\nMetadata for EcoAppScores.Assembly.Mechanisms.xlsx\n\nThis spreadsheet contains the values for dispersal capacity, niche breadth, and species interactions for each taxon used in this study, along with their respective nitrogen and phosphorus index scores.\n\n1.       Column level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Order | character/text | Taxonomic Order of the given taxon |\n\n| Family | character/text | Taxonomic Family of the given taxon |\n\n| Taxa| character/text | Taxonomic group, either family, subfamily, tribe, or genus, of the scored taxon. |\n\n| Niche Breadth | numeric | Quantified niche breadth based on weighted standard deviations. |\n\n| Dispersal capacity | numeric | Species propensity index, the metric for dispersal capacity, for the given taxon. |\n\n| Species interactions | numeric | Number of substantial species associations determined through Hierarchical Modelling of Species Communities. Calculations of these values can be found in the main manuscript text. |\n\n| Nitrogen Index Score | numeric | Nitrogen index score (1-10) determined for the study based on taxon’s nitrogen optimum. |\n\n| Phosphorus Index Score | numeric | Phosphorus index score (1-10) determined for the study based on taxon’s phosphorus optimum. |\n\n \n\n \n\n \n\n \n\n "}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20418171","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2026-05-27T20:59:12Z","registered":"2026-05-27T20:59:13Z","published":null,"updated":"2026-09-14T18:40:15Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20418172","type":"dois","attributes":{"doi":"10.5281/zenodo.20418172","identifiers":[{"identifier":"oai:zenodo.org:20418172","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01g9vbr38","name":"Oklahoma State University","affiliationIdentifierScheme":"ROR"}],"givenName":"Sam","familyName":"Miess","name":"Miess, Sam","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0008-8240-918X"}]}],"titles":[{"title":"Scripts and datasets associated with \"Incorporating community assembly mechanisms in biotic indices improves index performance\" in Ecological Applications"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-05-27","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"ScholarlyArticle","resourceTypeGeneral":"JournalArticle","citeproc":"article-journal","bibtex":"article","ris":"JOUR","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20418171","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"README\n\nThe following information serves as the metadata for the files associated with “Incorporating community assembly mechanisms in biotic indices improves index performance” published in Ecological Applications. All analyses can be run in Rstudio with the “EcoApp2025pubCode.R” and “EcoApp25.R” files.\n\nMetadata for Data.for.EcoApp25.xlsx\n\nOverview: Workbook contains five sheets: `Build`, `testdata`, `AllMech`, `Family2`, and `Genus2`. Blank cells indicate missing, unavailable, or not-applicable values unless otherwise specified. Numeric zeros in taxon-count columns indicate that zero individuals of that taxon were recorded for that sample. Taxonomic columns may include genera, families, tribes, or other operational taxonomic units, depending on the taxonomic resolution available in the source data.\n\n1.       Sheet: Build\n\nDescription: Site-level stream macroinvertebrate, water-quality, habitat, and taxon-abundance data from the Oklahoma Conservation Commission’s Rotating Basin Monitoring Program. Each row represents one biological sample or site-by-index-period sample. Early columns describe sampling location, date/season, water chemistry, and habitat metrics. Remaining columns are taxon abundance/count columns.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| NumOrder | numeric/integer | Row or sample ordering number used in the compiled dataset. |\n\n| SiteName | categorical/text | Name of the stream or sampling site. |\n\n| WBID | categorical/text | Site identifier code |\n\n| SAMPLE | categorical/text | Composite sample identifier, generally combining WBID, index period, and year. |\n\n| Basin | categorical/text | Major drainage basin containing the sampling site. |\n\n| SAMPLE.1 | categorical/text | Duplicate or alternate sample identifier|\n\n| L3 Ecoregion | categorical/text | Level III ecoregion name. |\n\n| Latitude | numeric | Site latitude in decimal degrees. |\n\n| Longitude | numeric | Site longitude in decimal degrees. |\n\n| LegalDesc | categorical/text | Legal land description of the sampling location, where available. |\n\n| County | categorical/text | County containing the sampling site. |\n\n| Ecoregion | categorical/text | Ecoregion name or abbreviation used in the compiled dataset. Values include full ecoregion names and abbreviated codes. |\n\n| Modified Ecoregion | categorical/text | Modified ecoregion classification/code where available. Example codes include `CGP`, `SWT`, `FH`, `CIP`, `CT`, `OH`, `AV`, `BM`, `OM`, `SCP`, and `ECTP`. |\n\n| Date | date | Sample collection date. |\n\n| Index | categorical/text | Seasonal index period for the sample, e.g., `Summer` or `Winter`. |\n\n| Year | numeric/integer | Calendar year associated with the sample. |\n\n| IndexYear | categorical/text | Combined index period and year, e.g., `Summer2001`. |\n\n| DO (mg/L) | numeric | Dissolved oxygen concentration, in milligrams per liter. |\n\n| Temp (°C) | numeric | Water temperature, in degrees Celsius. |\n\n| Turbidity (NTU) | numeric | Turbidity, in nephelometric turbidity units. |\n\n| Alkalinity (CaCO3) | numeric | Alkalinity reported as calcium carbonate. Units presumed mg/L as CaCO3. |\n\n| Conductivity | numeric | Specific conductance/conductivity in µS/cm. |\n\n| pH (SU) | numeric | pH in standard units. |\n\n| Flow | numeric/categorical | Field flow measurement or flow condition in cubic feet per second|\n\n| BaseFlow | numeric/categorical | Baseflow measurement or flow condition in cubic feet per second|\n\n| DOPercSat | numeric | Dissolved oxygen percent saturation. |\n\n| CBOD (mg/L) | numeric | Carbonaceous biochemical oxygen demand, in milligrams per liter. |\n\n| Chloride (mg/L) | numeric | Chloride concentration, in milligrams per liter. |\n\n| Sulfate (mg/L) | numeric | Sulfate concentration, in milligrams per liter. |\n\n| Ammonia (mg/L) | numeric | Ammonia concentration, in milligrams per liter. |\n\n| Nitrate (mg/L) | numeric | Nitrate concentration, in milligrams per liter. |\n\n| Nitrite (mg/L) | numeric | Nitrite concentration, in milligrams per liter. |\n\n| TKN (mg/L) | numeric | Total Kjeldahl nitrogen, in milligrams per liter. |\n\n| TotOrthoPhos (mg/L) | numeric | Total orthophosphate concentration, in milligrams per liter. |\n\n| Total Phosphorus (mg/L) | numeric | Total phosphorus concentration, in milligrams per liter. |\n\n| TSS (mg/L) | numeric | Total suspended solids, in milligrams per liter. |\n\n| TotDisSolids (mg/L) | numeric | Total dissolved solids, in milligrams per liter. |\n\n| Hardness (mg/L) | numeric | Water hardness, in milligrams per liter, likely as CaCO3. |\n\n| Enterococcus | numeric | Enterococcus bacteria measurement. Units should be confirmed from source documentation, commonly colony-forming units or MPN per 100 mL. |\n\n| E.Coli | numeric | Escherichia coli measurement. Units should be confirmed from source documentation, commonly colony-forming units or MPN per 100 mL. |\n\n| Instream Cover | numeric | Habitat score for instream cover. Higher values indicate greater habitat condition/availability according to the source scoring system. |\n\n| Pool Bottom Substrate | numeric | Habitat score for pool-bottom substrate condition. |\n\n| Pool Variability | numeric | Habitat score for pool variability. |\n\n| Canopy Cover Shading | numeric | Habitat score for canopy cover or shading. |\n\n| Presence of Rocky Runs or Riffles | numeric | Habitat score representing presence/quality of rocky runs or riffles. |\n\n| FlowHab | numeric | Habitat score related to flow conditions or flow habitat. |\n\n| Channel Alteration | numeric | Habitat score for degree of channel alteration. |\n\n| Channel Sinuosity | numeric | Habitat score for channel sinuosity. |\n\n| Bank Stability | numeric | Habitat score for bank stability. |\n\n| Bank Vegetation Stability | numeric | Habitat score for bank vegetation stability. |\n\n| Streamside Cover | numeric | Habitat score for riparian or streamside cover. |\n\n| Total Points | numeric | Total habitat score, calculated from component habitat metrics. |\n\n| Stenelmis through Lepidostomatidae | numeric/integer | All remaining columns are named for macroinvertebrate taxa or operational taxonomic units and represent the number of individuals recorded for that taxon in the sample. Taxonomic resolution varies among columns and may include genus, family, tribe, or ambiguous names. Examples include `Stenelmis`, `Chironomini`, `Orthocladiinae`, `Baetis`, `Hydropsyche`, and `Lepidostomatidae`. Blank cells indicate missing or unavailable taxon-count information; zeros indicate that no individuals of that taxon were recorded for that sample. |\n\n2.       Sheet: testdata\n\nDescription: Long-format modeling or analysis dataset containing site/sample identifiers, ecoregion, disturbance classification, taxonomic identity, abundance, habitat, water-quality variables, and biological index values as compiled from the Environmental Protection Agency’s National Rivers and Streams Assessment.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| UID | categorical/text| Unique site or sample identifier. |\n\n| Ecoregion | categorical/text | Ecoregion associated with the sample. Values include ecoregion names; capitalization is not fully standardized. |\n\n| DispersalCut | numeric | Dispersal-threshold or cutoff value used in classification/modeling for previous integration approach. This is no longer used in the final methodology|\n\n| NicheCut | numeric/integer | Niche-breadth cutoff value used in classification/modeling for previous integration approach. This is no longer used in the final methodology|\n\n| CooccurCut | numeric/integer | Co-occurrence/interactions cutoff value used in classification/modeling for previous integration approach. This is no longer used in the final methodology. |\n\n| Year | numeric/integer | Year of sample collection. |\n\n| Month | numeric/integer | Month of sample collection, coded 1–12. |\n\n| Classification | categorical/text | Disturbance classification assigned to the sample by the EPA. Observed values include `Least Disturbed`, `Intermediate Disturbance`, `Most Disturbed`, and `?`. The value `?` indicates unknown or unresolved classification. |\n\n| Family | categorical/text | Macroinvertebrate family name, when available. |\n\n| Species | categorical/text | Taxon name used in the analysis. Despite the column name, values may represent genus, family, tribe, or other taxonomic levels depending on identification resolution. The naming convention “species” was used as it helped expediate table joins; it in no way indicates the true species name.|\n\n| Count | numeric | Number of individuals recorded for the taxon in the sample. |\n\n| Habitat | numeric | Habitat score or habitat metric associated with the sample. Higher/lower interpretation depends on the source scoring system. |\n\n| DO | numeric | Dissolved oxygen concentration in mg/L|\n\n| Temp | numeric | Water temperature, in degrees Celsius. |\n\n| pH | numeric | pH in standard units. |\n\n| Cond | numeric | Conductivity/specific conductance in µS/cm. |\n\n| Turb | numeric | Turbidity in NTU. |\n\n| TP | numeric | Total phosphorus concentration in µg/L |\n\n| NH4 | numeric | Ammonium/ammonia nitrogen measurement in mg/L |\n\n| NO3 | numeric | Nitrate measurement in mg/L |\n\n| TN | numeric | Total nitrogen measurement in mg/L |\n\n| PTV | numeric | Pollution tolerance value or biological tolerance value associated with the taxon.|\n\n \n\n3.       Sheet: AllMech\n\nDescription: Taxon-level trait/mechanism table describing taxonomic identity, occurrence, interaction counts (from previous interaction metric), niche breadth, dispersal score, and assigned dispersal type.\n\n \n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Taxa | categorical/text | Taxon name. Values may represent genus, family, or another operational taxonomic unit. |\n\n| Family | categorical/text | Taxonomic family associated with “Taxa”, where available. |\n\n| Order | categorical/text | Taxonomic order associated with “Taxa”, where available. |\n\n| Sites | numeric/integer | Number of sites where the taxon occurred or was included. In this sheet, values appear to be 1 for all rows. |\n\n| Interactions | numeric/integer | Number of co-occurrence interactions, as determined via Veech’s Probabilistic model for co-occurrence. These values were calculated during a previous analytical approach but are not useful for the current manuscript.|\n\n| NicheBreadth | numeric | Estimated niche breadth value for the taxon. Higher values indicate broader inferred environmental or ecological niche breadth, as calculated based on weighted standard deviations|\n\n| SFP | numeric | Species flight propensity, the metric used for approximating dispersal capacity. |\n\n| DispersalType | categorical/text | Assigned dispersal type. Observed values are `Aerial` and `Aquatic`. |\n\n4.       Sheet: Family2\n\nDescription: Family-level optima by ecoregion. Each row represents one family-level taxon within an ecoregion. Numeric values represent estimated environmental optima, based on the given abiotic variable. Some of these values were log-transformed before optima were calculated; as such, some values appear negative. Family-level values were used when genus-level optima were not available/collected.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Ecoregion | categorical/text | Ecoregion code for the estimate. Observed codes include `All`, `AV`, `BM`, `CGP`, `CIP`, `CT`, `OH`, `OM`, `SCP`, and `SWT`. |\n\n| Species | categorical/text | Taxon name. In this sheet, values appear primarily to be family-level names despite the column name `Species`. |\n\n| Do | numeric | Estimated optimum for dissolved oxygen. Note capitalization differs from `DO` elsewhere. |\n\n| Temp | numeric | Estimated optimum for water temperature. |\n\n| Turbidity | numeric | Estimated optimum for turbidity. |\n\n| NH4 | numeric | Estimated optimum for ammonium/ammonia. |\n\n| Conductivity | numeric | Estimated optimum for conductivity. |\n\n| pH | numeric | Estimated optimum for pH. |\n\n| Ammonia | numeric | Estimated optimum for ammonia. |\n\n| Nitrate | numeric | Estimated optimum for nitrate. |\n\n| Nitrite | numeric | Estimated optimum for nitrite. |\n\n| TKN | numeric | Estimated optimum for total Kjeldahl nitrogen. |\n\n| TP | numeric | Estimated optimum for total phosphorus. |\n\n| E.coli | numeric | Estimated optimum for Escherichia coli. |\n\n| Instream | numeric | Estimated optimum for instream habitat condition. |\n\n| Habitat | numeric | Estimated optimum for overall habitat condition. |\n\n \n\nb.       Ecoregion code definitions\n\n| Code | Ecoregion |\n\n| All | All ecoregions combined |\n\n| AV | Arkansas Valley |\n\n| BM | Boston Mountains |\n\n| CGP | Central Great Plains |\n\n| CIP | Central Irregular Plains |\n\n| CT | Cross Timbers |\n\n| OH | Ozark Highlands |\n\n| OM | Ouachita Mountains |\n\n| SCP | South Central Plains |\n\n| SWT | Southwestern Tablelands |\n\n \n\n5.       Sheet: Genus2\n\nDescription: Genus-level optima by ecoregion. Each row represents one family-level taxon within an ecoregion. Numeric values represent estimated environmental optima, based on the given abiotic variable. Some of these values were log-transformed before optima were calculated; as such, some values appear negative. Family-level values were used when genus-level optima were not available/collected.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Species | categorical/text | Taxon name. Despite the column name `Species`, values may represent genus, family, or another operational taxonomic unit depending on taxonomic resolution. |\n\n| Do | numeric | Estimated optimum for dissolved oxygen. Note capitalization differs from `DO` elsewhere. |\n\n| Temp | numeric | Estimated optimum for water temperature. |\n\n| NH4 | numeric | Estimated optimum for ammonium/ammonia. |\n\n| Turbidity | numeric | Estimated optimum for turbidity. |\n\n| Conductivity | numeric | Estimated optimum for conductivity. |\n\n| pH | numeric | Estimated association optimum for pH. |\n\n| Ammonia | numeric | Estimated optimum for ammonia. |\n\n| Nitrate | numeric | Estimated optimum for nitrate. |\n\n| Nitrite | numeric | Estimated ptimum for nitrite. |\n\n| TKN | numeric | Estimated optimum for total Kjeldahl nitrogen. |\n\n| TP | numeric | Estimated optimum for total phosphorus. |\n\n| E.coli | numeric | Estimated optimum for Escherichia coli. |\n\n| Instream | numeric | Estimated optimum for instream habitat condition. |\n\n| Habitat | numeric | Estimated optimum for overall habitat condition. |\n\n| Ecoregion | categorical/text | Ecoregion code for the estimate. Observed codes include `All`, `AV`, `BM`, `CGP`, `CIP`, `CT`, `OH`, `OM`, `SCP`, and `SWT`. |\n\nb.       Ecoregion code definitions\n\n| Code | Ecoregion |\n\n| All | All ecoregions combined |\n\n| AV | Arkansas Valley |\n\n| BM | Boston Mountains |\n\n| CGP | Central Great Plains |\n\n| CIP | Central Irregular Plains |\n\n| CT | Cross Timbers |\n\n| OH | Ozark Highlands |\n\n| OM | Ouachita Mountains |\n\n| SCP | South Central Plains |\n\n| SWT | Southwestern Tablelands |\n\n \n\n6.       Missing values and special codes\n\n·       Blank cells indicate missing, unavailable, or not-applicable data unless otherwise stated.\n\n·       In taxon-abundance columns, numeric `0` indicates no individuals of that taxon were recorded for the sample.\n\n·       The value `?` in `testdata$Classification` indicates unresolved or unknown disturbance classification.\n\n·       Taxonomic columns may include multiple taxonomic resolutions. The column name `Species` does not always indicate species-level identification.\n\n·       Ecoregion names/codes are not fully standardized across sheets; some sheets use full names, while others use abbreviations.\n\n \n\n7.       Citation information\n\nThe “Build” sheet contains data compiled from the Oklahoma Conservation Commission’s Rotating Basin Monitoring Program. The reports from which the data were extracted can be found here:\n\n \n\nWQ-Statewide Rotating Basin Monitoring Program - Oklahoma Conservation\n\nCommission. 2021, July 2.https://conservation.ok.gov/wq-statewide-rotating-basin-monitoring-program/\n\n \n\nSpecifics regarding how each variable was measured or otherwise collected can be found in their standard operating procedures here:\n\n \n\nOklahoma Conservation Commission. 2023. Standard operating procedures for\n\nwater quality monitoring and measurement activities. Oklahoma Conservation Commission Water Quality Division. https://conservation.ok.gov/wp-content/uploads/2023/08/sop.pdf\n\n \n\nThe “testdata” sheet contains data compiled from the Environmental Protection Agency’s National Rivers and Streams Assessment. The datasets from which data were pulled can be found here:\n\n \n\nUS EPA. 2015, June 23. National Rivers and Streams Assessment. Overviews and\n\nFactsheets. https://www.epa.gov/national-aquatic-resource-surveys/nrsa.\n\n \n\nSpecifics regarding how each variable was measured or otherwise collected by US EPA can be found in their standard operating procedures here:\n\n \n\nUSEPA. 2022. National Rivers and Streams Assessment 2023/24: Field Operations\n\nManual – Wadeable. U.S. Environmental Protection Agency, Office of Water, Washington, DC. https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-streams-assessment-2023-24-field-operations\n\nThe Species Flight Propensity (SFP) index, which was used for quantifying dispersal capacity, was calculated based on the scoring proposed by Sarremejane et al (2017). This index can be found here:\n\nSarremejane, R., H. Mykrä, N. Bonada, J. Aroviita, and T. Muotka. 2017. Habitat\n\nconnectivity and dispersal ability drive the assembly mechanisms of macroinvertebrate communities in river networks. Freshwater Biology 62:1073–1082.\n\n \n\n \n\nMetadata for EcoApp25SBIData.xlsx\n\nThis .xlsx contains six sheets: `OCC`, `Variables`, `EPA`, `SBItest`, `Groups_Mechanisms`, and `SBI`. Blank cells indicate missing, unavailable, or not-applicable values unless otherwise specified. In wide-format taxon-abundance columns, blank cells indicate that the taxon was not recorded in that sample and should be interpreted as zero abundance for community-composition analyses, unless otherwise noted in the analysis code.\n\n1.       Sheet: OCC\n\nDescription: Wide-format stream macroinvertebrate, water-quality, habitat, and site-level dataset from the Oklahoma Conservation Commission’s Rotating Basin Monitoring Program. Each row represents one stream sample. Early columns describe sample identity, geographic location, region/ecoregion, collection timing, water chemistry, and habitat metrics. All columns beginning with `Stenelmis` and continuing through `Lepidostomatidae` are taxon-abundance columns.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| NumOrder | numeric/integer | Row-ordering number used in the compiled dataset. |\n\n| SiteName | categorical/text | Stream or sampling-site name. |\n\n| WBID | categorical/text | Site identifier. |\n\n| SAMPLE | categorical/text | Composite sample identifier, generally combining WBID, index period, and year. |\n\n| Basin | categorical/text | Major drainage basin associated with the sample. |\n\n| SAMPLE2 | categorical/text | Alternate or duplicate composite sample identifier. |\n\n| L3 Ecoregion | categorical/text | Level III ecoregion name. |\n\n| Region | categorical/text | Regional classification used for analysis. Values include named regions such as `Ozark Highlands`, `Boston Mountains`, `Central Great Plains`, and others. |\n\n| Aggregate | categorical/text | Aggregated regional/ecological grouping used for analysis. Observed codes include `SAP`, `NAP`, `TPL`, `SPL`, `UMW`, `WMT`, `NPL`, and `XER`. |\n\n| DispersalCut | numeric | Dispersal-score cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| NicheCut | numeric | Niche-breadth cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| CooccurCut | numeric | Co-occurrence/interactions cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| Latitude | numeric | Sampling-site latitude in decimal degrees. |\n\n| Longitude | numeric | Sampling-site longitude in decimal degrees. |\n\n| LegalDesc | categorical/text | Legal land description of the sampling location, where available. |\n\n| County | categorical/text | County containing the sampling site. |\n\n| Ecoregion | categorical/text | Ecoregion classification used in the dataset. |\n\n| Date | date | Sample collection date. |\n\n| Index | categorical/text | Seasonal index period for the sample, e.g., `Summer` or `Winter`. |\n\n| Year | numeric/integer | Calendar year of sample collection. |\n\n| IndexYear | categorical/text | Combined index period and year, e.g., `Summer2001`. |\n\n| DO | numeric | Dissolved oxygen concentration. Units are presumed mg/L. |\n\n| Temp | numeric | Water temperature, in degrees Celsius. |\n\n| Turb | numeric | Turbidity. Units are NTU. |\n\n| Cond | numeric | Conductivity or specific conductance in µS/cm. |\n\n| pH | numeric | pH in standard units. |\n\n| Habitat | numeric | Overall habitat score or habitat-condition metric. Higher/lower interpretation follows the source scoring protocol. |\n\n| TP | numeric | Total phosphorus concentration, units are in ug/L|\n\n| TN | numeric | Total nitrogen concentration, units in mg/L|\n\n| NH4 | numeric | Ammonium/ammonia measurement, units in mg/L|\n\n| NO3 | numeric | Nitrate measurement, units in mg/L|\n\n| NO2 | numeric | Nitrite measurement, units in mg/L|\n\n| Flow | numeric/categorical | Field flow measurement or flow condition, units in cubic feet per second|\n\n| Sulfate (mg/L) | numeric | Sulfate concentration, in milligrams per liter. |\n\n| TotOrthoPhos (mg/L) | numeric | Total orthophosphate concentration, in milligrams per liter. |\n\n| TSS (mg/L) | numeric | Total suspended solids, in milligrams per liter. |\n\n| TotDisSolids (mg/L) | numeric | Total dissolved solids, in milligrams per liter. |\n\n| Hardness (mg/L) | numeric | Water hardness, in milligrams per liter, likely as CaCO3. |\n\n| Enterococcus | numeric | Enterococcus bacteria measurement. Units should be verified from source documentation, commonly CFU or MPN per 100 mL. |\n\n| E.Coli | numeric | Escherichia coli bacteria measurement. Units should be verified from source documentation, commonly CFU or MPN per 100 mL. |\n\n| Instream Cover | numeric | Habitat score for instream cover. |\n\n| Pool Bottom Substrate | numeric | Habitat score for pool-bottom substrate condition. |\n\n| Pool Variability | numeric | Habitat score for pool variability. |\n\n| Canopy Cover Shading | numeric | Habitat score for canopy cover or shading. |\n\n| Presence of Rocky Runs or Riffles | numeric | Habitat score representing presence or quality of rocky runs/riffles. |\n\n| FlowHab | numeric | Habitat score related to flow habitat. |\n\n| Channel Alteration | numeric | Habitat score for degree of channel alteration. |\n\n| Channel Sinuosity | numeric | Habitat score for channel sinuosity. |\n\n| Bank Stability | numeric | Habitat score for streambank stability. |\n\n| Bank Vegetation Stability | numeric | Habitat score for bank vegetation stability. |\n\n| Streamside Cover | numeric | Habitat score for riparian or streamside cover. |\n\n| Stenelmis through Lepidostomatidae | numeric/integer | All remaining columns are named for macroinvertebrate taxa or operational taxonomic units and represent the abundance/count of individuals recorded for that taxon in the sample. Taxonomic resolution varies among columns and may include genus, family, subfamily, tribe, or ambiguous operational names. Blank cells indicate that the taxon was not recorded in that sample and should be interpreted as zero abundance for community-composition analyses. |\n\n2.       Sheet: Variables\n\nDescription: Lookup sheet identifying variables used in the compiled analysis dataset and taxa included in the macroinvertebrate abundance matrix.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Columns | categorical/text | Names of selected analytical variables used elsewhere in the workbook, including sample identifiers, regional classifications, water-quality variables, habitat variables, taxon names, and count fields. |\n\n| Bugs | categorical/text | Macroinvertebrate taxon names included in the abundance matrix. Taxa may be identified to genus, family, subfamily, tribe, or another operational taxonomic unit depending on identification resolution. |\n\n \n\n3.       Sheet: EPA\n\nDescription: Long-format macroinvertebrate dataset compiled from the Environmental Protection Agency’s National Rivers and Streams Assessment. Each row represents the abundance/count of one taxon in one stream sample, with associated site/sample metadata, region/ecoregion, water-quality variables, habitat score, chloride, and tolerance-value information.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| SAMPLE | categorical/text | Sample identifier. |\n\n| Ecoregion | categorical/text | Ecoregion name associated with the sample. Values are generally uppercase names. |\n\n| Region | categorical/text | Regional classification used for analysis, e.g., `Boston Mountains`, `Ozark Highlands`, `Central Irregular Plains`, and others. |\n\n| Aggregate | categorical/text | Aggregated regional/ecological grouping used for analysis. Observed codes include `SAP`, `NAP`, `TPL`, `SPL`, `UMW`, `WMT`, `NPL`, and `XER`. |\n\n| DispersalCut | numeric | Dispersal-score cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| NicheCut | numeric | Niche-breadth cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| CooccurCut | numeric | Co-occurrence/interactions cutoff used for assigning or filtering taxa in the analysis. This was part of an earlier analytical approach and is not used in the current analytical framework.|\n\n| Year | numeric/integer | Calendar year of sample collection. |\n\n| Month | numeric/integer | Month of sample collection, coded 1–12. |\n\n| Classification | categorical/text | Disturbance or condition classification assigned to the sample, where available. Blank cells indicate no classification was assigned or available. |\n\n| Family | categorical/text | Taxonomic family assigned to the organism. |\n\n| Taxa | categorical/text | Taxon name used in analysis. Values may represent genus, family, or another operational taxonomic unit depending on taxonomic resolution. |\n\n| Count | numeric/integer | Number of individuals of the taxon recorded in the sample. |\n\n| Habitat | numeric | Habitat score or habitat metric associated with the sample. |\n\n| DO | numeric | Dissolved oxygen concentration in  mg/L. |\n\n| Temp | numeric | Water temperature, in degrees Celsius. |\n\n| pH | numeric | pH in standard units. |\n\n| Cond | numeric | Conductivity or specific conductance in µS/cm. |\n\n| Turb | numeric | Turbidity in NTU. |\n\n| TP | numeric | Total phosphorus concentration in micrograms per liter|\n\n| NH4 | numeric | Ammonium/ammonia measurement in mg/L|\n\n| NO3 | numeric | Nitrate measurement in mg/L|\n\n| TN | numeric | Total nitrogen concentration in mg/L|\n\n| Chloride | numeric | Chloride concentration in mg/L. |\n\n| PTV | numeric | Pollution tolerance value assigned to the taxon. Higher values generally indicate greater tolerance to pollution or environmental stress, as determined by the EPA|\n\n \n\n4.       Sheet: SBItest\n\nDescription: Long-format dataset used for testing the  Salt Belt Index/biotic index. Each row represents a taxon occurrence/count in a sample, paired with chloride concentration and regional grouping.\n\na.       Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| UID | categorical/text or numeric | Unique sample or site identifier. Treat as an identifier, not as a continuous numeric variable. |\n\n| Region | categorical/text | Aggregated regional grouping used for analysis. Observed codes include values such as `SAP` and `NAP`. |\n\n| Chloride | numeric | Chloride concentration associated with the sample in mg/L. |\n\n| Family2 | categorical/text | Harmonized or analysis-ready family name for the taxon. |\n\n| Taxa.org | categorical/text | Original taxon name or original taxonomic label before harmonization. |\n\n| Taxa | categorical/text | Harmonized taxon name used in analysis. |\n\n| Count | numeric/integer | Number of individuals of the taxon recorded in the sample. |\n\n \n\n5.       Sheet: Groups_Mechanisms\n\nDescription: Taxon-level trait/mechanism table describing taxonomic identity, occurrence, interaction counts, niche breadth, dispersal score, and assigned dispersal mode.\n\na.        Column-level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Taxa | categorical/text | Taxon name. Values may represent genus, family, or another operational taxonomic unit. |\n\n| Family | categorical/text | Taxonomic family associated with `Taxa`, where available. |\n\n| Order | categorical/text | Taxonomic order associated with `Taxa`, where available. |\n\n| Sites | numeric/integer | Number of sites or site groups associated with the taxon in this table. |\n\n| Interactions | numeric/integer | Number of co-occurrences as determined using Veech’s Probabilistic Model for Cooccurrence. These were calculated for an earlier analytical approach but are not used for the current workflow.|\n\n| NicheBreadth | numeric | Estimated niche-breadth value for the taxon, based on weighted standard deviations. Higher values indicate broader inferred environmental or ecological niche breadth. |\n\n| SFP | numeric | Species Flight Propensity Index value used as a metric for dispersal capacity. |\n\n| DispersalType | categorical/text | Assigned dispersal type. Observed values include `Aerial` and `Aquatic`. |\n\n \n\n6.       Sheet: SBI\n\nDescription: Taxon-level Salt Belt Index or stressor-tolerance table. Each row represents a macroinvertebrate taxon and its assigned index/tolerance values by aggregated regional group. Regional columns contain numeric taxon scores for each region where a value was available.\n\na.        Column-level metadata\n\n \n\n| Column name | Data type | Description / interpretation |\n\n| Phylum | categorical/text | Taxonomic phylum. |\n\n| Class | categorical/text | Taxonomic class. |\n\n| Order | categorical/text | Taxonomic order. |\n\n| Family | categorical/text | Taxonomic family. |\n\n| Taxa.original | categorical/text | Original taxon label before harmonization. |\n\n| Taxa | categorical/text | Harmonized taxon name used in analysis. |\n\n| SAP | numeric | Taxon SBI/tolerance value for the SAP regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| NAP | numeric | Taxon SBI/tolerance value for the NAP regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| UMW | numeric | Taxon SBI/tolerance value for the UMW regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| TPL | numeric | Taxon SBI/tolerance value for the TPL regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| SPL | numeric | Taxon SBI/tolerance value for the SPL regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| XER | numeric | Taxon SBI/tolerance value for the XER regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| WMT | numeric | Taxon SBI/tolerance value for the WMT regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n| NPL | numeric | Taxon SBI/tolerance value for the NPL regional group. Blank cells indicate no value was assigned or available for that taxon-region combination. |\n\n \n\n7.       Regional code notes\n\n| Code | Description |\n\n| Code | Ecoregion |\n\n| All | All ecoregions combined |\n\n| AV | Arkansas Valley |\n\n| BM | Boston Mountains |\n\n| CGP | Central Great Plains |\n\n| CIP | Central Irregular Plains |\n\n| CT | Cross Timbers |\n\n| OH | Ozark Highlands |\n\n| OM | Ouachita Mountains |\n\n| SCP | South Central Plains |\n\n| SWT | Southwestern Tablelands |\n\n \n\n8.       Missing values and special coding\n\n·       Blank cells indicate missing, unavailable, or not-applicable data unless otherwise stated.\n\n·       In `OCC`, taxon columns from `Stenelmis` through `Lepidostomatidae` are wide-format abundance/count columns. Blank cells indicate that the taxon was not recorded in that sample and should be treated as zero abundance for community analyses.\n\n·       In `SBI`, blank regional score cells indicate no SBI/tolerance value was assigned or available for that taxon-region combination.\n\n·        Taxonomic columns may include mixed taxonomic resolution. Names may represent genus, family, subfamily, tribe, or another operational taxonomic unit.\n\n·       The column name `Taxa` is used broadly and does not always imply species-level resolution.\n\n·       `SFP`, `DispersalCut`, `NicheCut`, and `CooccurCut` should be defined in the README using the exact terminology from the manuscript or analysis code.\n\n \n\n9.       Citation information\n\nThe “OCC” sheet contains data compiled from the Oklahoma Conservation Commission’s Rotating Basin Monitoring Program. The reports from which the data were extracted can be found here:\n\n \n\nWQ-Statewide Rotating Basin Monitoring Program - Oklahoma Conservation\n\nCommission. 2021, July 2.https://conservation.ok.gov/wq-statewide-rotating-basin-monitoring-program/\n\n \n\nSpecifics regarding how each variable was measured or otherwise collected can be found in their standard operating procedures here:\n\n \n\nOklahoma Conservation Commission. 2023. Standard operating procedures for\n\nwater quality monitoring and measurement activities. Oklahoma Conservation Commission Water Quality Division. https://conservation.ok.gov/wp-content/uploads/2023/08/sop.pdf\n\n \n\nThe “EPA” sheet contains data compiled from the Environmental Protection Agency’s National Rivers and Streams Assessment. The datasets from which data were pulled can be found here:\n\n \n\nUS EPA. 2015, June 23. National Rivers and Streams Assessment. Overviews and\n\nFactsheets. https://www.epa.gov/national-aquatic-resource-surveys/nrsa.\n\n \n\nSpecifics regarding how each variable was measured or otherwise collected by US EPA can be found in their standard operating procedures here:\n\n \n\nUSEPA. 2022. National Rivers and Streams Assessment 2023/24: Field Operations\n\nManual – Wadeable. U.S. Environmental Protection Agency, Office of Water, Washington, DC. https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-streams-assessment-2023-24-field-operations\n\nThe Species Flight Propensity (SFP) index, which was used for quantifying dispersal capacity, was calculated based on the scoring proposed by Sarremejane et al (2017). This index can be found here:\n\nSarremejane, R., H. Mykrä, N. Bonada, J. Aroviita, and T. Muotka. 2017. Habitat\n\nconnectivity and dispersal ability drive the assembly mechanisms of macroinvertebrate communities in river networks. Freshwater Biology 62:1073–1082.\n\nThe “SBI” sheet contains scores and values from Miess and Dzialowski (2024), and information regarding these data can be found here:\n\nMiess, S., and A. R. Dzialowski. 2024. Salt Belt Index (SBI): A biotic index for streams\n\nwithin the North American “salt belt,” with proposed baseline chloride thresholds. Science of The Total Environment 941:173726.\n\n \n\nMetadata for EcoAppScores.Assembly.Mechanisms.xlsx\n\nThis spreadsheet contains the values for dispersal capacity, niche breadth, and species interactions for each taxon used in this study, along with their respective nitrogen and phosphorus index scores.\n\n1.       Column level metadata\n\n| Column name | Data type | Description / interpretation |\n\n| Order | character/text | Taxonomic Order of the given taxon |\n\n| Family | character/text | Taxonomic Family of the given taxon |\n\n| Taxa| character/text | Taxonomic group, either family, subfamily, tribe, or genus, of the scored taxon. |\n\n| Niche Breadth | numeric | Quantified niche breadth based on weighted standard deviations. |\n\n| Dispersal capacity | numeric | Species propensity index, the metric for dispersal capacity, for the given taxon. |\n\n| Species interactions | numeric | Number of substantial species associations determined through Hierarchical Modelling of Species Communities. Calculations of these values can be found in the main manuscript text. |\n\n| Nitrogen Index Score | numeric | Nitrogen index score (1-10) determined for the study based on taxon’s nitrogen optimum. |\n\n| Phosphorus Index Score | numeric | Phosphorus index score (1-10) determined for the study based on taxon’s phosphorus optimum. |\n\n \n\n \n\n \n\n \n\n "}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20418172","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-05-27T20:59:12Z","registered":"2026-05-27T20:59:12Z","published":null,"updated":"2026-09-14T18:40:14Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.17605/osf.io/y42d9","type":"dois","attributes":{"doi":"10.17605/osf.io/y42d9","identifiers":[{"identifier":"https://osf.io/y42d9","identifierType":"URL"}],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01g9vbr38","name":"Oklahoma State University","affiliationIdentifierScheme":"ROR"}],"name":"Demetri Monroe","nameIdentifiers":[{"nameIdentifierScheme":"URL","nameIdentifier":"https://osf.io/6ucp2"}]},{"nameType":"Personal","name":"Grace Kirsch","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0005-5089-7770"},{"nameIdentifierScheme":"URL","nameIdentifier":"https://osf.io/vfmnr"}],"affiliation":[]}],"titles":[{"title":"Orthobiologic Interventions and Return-to-Sport Timelines After ACL Reconstruction: A Scoping Review"}],"publisher":"OSF Registries","container":{},"publicationYear":2026,"subjects":[{"subject":"Orthopedics","subjectScheme":"bepress Digital Commons Three-Tiered Taxonomy"},{"subject":"Medicine and Health Sciences","subjectScheme":"bepress Digital Commons Three-Tiered Taxonomy"},{"subject":"Medical Specialties","subjectScheme":"bepress Digital Commons Three-Tiered Taxonomy"}],"contributors":[{"nameType":"Organizational","name":"Center for Open Science","nameIdentifiers":[{"nameIdentifierScheme":"URL","nameIdentifier":"https://cos.io/"},{"nameIdentifierScheme":"ROR","schemeUri":"https://ror.org","nameIdentifier":"https://ror.org/05d5mza29"}],"contributorType":"HostingInstitution","affiliation":[]}],"dates":[{"date":"2026-09-13","dateType":"Created"},{"date":"2026-09-13","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"StudyRegistration","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Pre-registration"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"https://osf.io/tkqn6","relatedIdentifierType":"URL"}],"relatedItems":[{"relationType":"IsVersionOf","relatedItemIdentifier":{"relatedItemIdentifier":"https://osf.io/tkqn6","relatedItemIdentifierType":"URL"},"relatedItemType":"Text","creators":[],"publisher":"OSF","publicationYear":"2026","titles":[{"title":"Orthobiologic Interventions and Return-to-Sport Timelines After ACL Reconstruction: A Scoping Review"}],"contributors":[]}],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"What is known from the literature about the comparative effect of orthobiologic interventions (PRP, BMAC, stem cell therapy) on return-to-sport timelines in athletes undergoing ACL reconstruction?\n\nThis is a scoping review, following the PRISMA-ScR reporting framework. Scoping methodology (rather than systematic review) was chosen because the aim is to map the extent and nature of existing evidence, not to pool effect estimates -- appropriate given the anticipated predominance of lower-tier evidence (case series/reports) over RCTs in this literature."}],"geoLocations":[],"fundingReferences":[],"url":"https://osf.io/y42d9/","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-09-13T20:44:26Z","registered":"2026-09-13T20:44:26Z","published":null,"updated":"2026-09-13T20:44:36Z"},"relationships":{"client":{"data":{"id":"cos.osf","type":"clients"}}}},{"id":"10.5281/zenodo.19354303","type":"dois","attributes":{"doi":"10.5281/zenodo.19354303","identifiers":[],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/035pkj773","name":"Ştefan cel Mare University of Suceava","affiliationIdentifierScheme":"ROR"}],"givenName":"Cristina","familyName":"Bleorțu","name":"Bleorțu, Cristina","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-1645-7932"}]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/05rdf8595","name":"Universidade de Vigo","affiliationIdentifierScheme":"ROR"}],"givenName":"Miguel","familyName":"Cuevas-Alonso","name":"Cuevas-Alonso, Miguel","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0001-7656-2374"}]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/03nawhv43","name":"University of California, Riverside","affiliationIdentifierScheme":"ROR"},{"name":"University of California Riverside"}],"givenName":"Covadonga","familyName":"Lamar Prieto","name":"Lamar Prieto, Covadonga","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-8533-5138"}]},{"nameType":"Personal","givenName":"Miriam","familyName":"Villazón Valbuena","name":"Villazón Valbuena, Miriam","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0003-2209-9307"}],"affiliation":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/045ntgf29","name":"Oklahoma State University System","affiliationIdentifierScheme":"ROR"}],"givenName":"Isabel","familyName":"Álvarez-Sancho","name":"Álvarez-Sancho, Isabel","nameIdentifiers":[]}],"titles":[{"title":"Book of Abstracts - IV SAnTINA Conference, Society for the Analysis of Cultural Topics and Linguistic Identities N'Asturies"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-03-31","dateType":"Issued"}],"language":"es","types":{"schemaOrg":"ScholarlyArticle","resourceTypeGeneral":"Text","citeproc":"article-journal","bibtex":"article","ris":"RPRT","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.19354304","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Coeditores\n\n \n\nCristina Bleorțu\n\nMiguel Cuevas Alonso\n\nIsabel Álvarez-Sancho\n\nCovadonga Lamar Prieto\n\nMiriam Villazón Valbuena\n\n \n\n \n\n \n\nÍndice\n\n \n\n \n\nHéctor Álvarez Mella (Observatorio Global del Español, Instituto Cervantes)\n\nConferencia plenaria: Actitudes lingüísticas, migración e identidad en Asturias: ¿juego de espejos o reajuste axiológico?. 5\n\n \n\nAramo Álvarez (University of California, Davis),  Cristina Bleorțu (Universitatea „Ștefan cel Mare” din Suceava), Daniela Hăisan (Universitatea „Ștefan cel Mare” din Suceava), Miriam Villazón Valbuena (University of California, Riverside), \u0026 Silviu Popescu \n\nMesa redonda: Linguistics 4.0: How AI is transforming the study of language. 7\n\n \n\nIngrid Cobos López (Universidad de Córdoba), Miguel Cuevas Alonso (Universidade de Vigo), Covadonga Lamar Prieto (University of California, Riverside), Katia Rolán González (Universidade de Vigo), Vanesa Rodríguez Tembrás (Universität Heidelberg), \u0026 María Vila Tarela (Universidade de Vigo)\n\nMesa redonda: Humanidades médicas y lenguas minorizadas. 9\n\n \n\nEnrique Álvarez (Florida State University), Isabel Álvarez Sancho (Oklahoma State University), Eva Álvarez Vázquez (University of Massachusetts - Amherst), Luke Bowe (Kenyon Colleg), Nuria Godón (Florida Atlantic University), Laura Lesta (Middlebury College), Alfredo Martínez Expósito (University of Melbourne), \u0026 Bill Nichols (University of Texas at Dallas)\n\nMesa redonda: Amigos/as de los estudios asturianos (Primera edición dedicada al Prof. José Colmeiro, Universidad de Auckland) 11\n\n \n\nElena Álvarez Rodríguez (Universidá d'Uviéu)\n\nNueves formes d’espresión nes llingües minorizaes en relación col casu asturianu. 13\n\n \n\nIsabel Álvarez Sancho (Oklahoma State University)\n\nImaginaciones exílicas en la cultura asturiana contemporánea: creación, desplazamiento y reescritura de la historia. 14\n\n \n\nEva Álvarez-Vázquez (University of Massachusetts, Amherst)\n\nEcologías cuir en el paisaje posindustrial asturiano: «Materia Frágil, Becoming Inmortal» de Kela Coto. 16\n\n \n\nPaul Cahill (Pomona College)\n\n«Un punto ciego en el mapa conocido como la tierra de Nonú»: Imagining Asturias in the Poetry of Laura Ramos. 17\n\n \n\nFrancisco Calvo del Olmo (Ludwig-Maximilians-Universität München) \u0026 Gonzalo Llamedo-Pandiella (Universidá d'Uviéu)\n\nLa intercomprensión románica como vía pal aprendizaxe y la normalización del asturianu y el mirandés. 19\n\n \n\nErik Ekman (Oklahoma State University)\n\nAsturias in the works of Alfonso X.. 21\n\n \n\nAlba García Rodríguez (Universidad Complutense de Madrid)\n\nSociedad, lengua y cultura asturianas en la literatura actual: Aitana Castaño y Alfonso Zapico. 22\n\n \n\nLuis González Fernández (École des Hautes Études Hispaniques et Ibériques-Casa de Velázquez)\n\n‘Me pusi la permanente, creyendo que era más guapa’: coplas tradicionales y modas en el peinar 23\n\n \n\nAlba González Sanz (Universidad de Oviedo)\n\nRedes transhistóricas e hibridez lingüística en la obra literaria de Paquita Suárez Coalla. 25\n\n \n\nLavinia Ienceanu (Universitatea „Ștefan cel Mare” din Suceava)\n\nDe «Bovary española» a figura de pura estirpe asturiana: una reaproximación a La Regenta. 26\n\n \n\nIsabel María Kentengian Osorio (The College of New Jersey)\n\nLos paisajes lingüísticos de los museos etnográficos de Asturias. 28\n\n \n\nCovadonga Lamar Prieto (University of California, Riverside)\n\n«Salutate Mariam»: identidad asturiana, reconquista y devoción mariana en el sermón del Día de Covadonga (Madrid, 1793) 29\n\n \n\nAdrián Martínez Expósito (Universidá d'Uviéu)\n\nAnálisis redaccional y propuesta d'aparatu críticu de Filoloxía d'autor de los Poemes y Traducciones Xaponeses n'Asturianu de Fernán-Coronas. 31\n\n \n\nRafael Maldonado de Guevara Delgado (Universidad Carlos III)\n\nAsturias y la resistencia lingüística y cultural de Puerto Rico. 32\n\n \n\nLlucía Menéndez Díaz (Universidá d'Uviéu)\n\nEl gatu na fraseoloxía asturiana: análisis semántico-cognitivu. 33\n\n \n\nMariam Nadirashvili (University of California, Riverside)\n\nMemoria como resistencia: mujeres en la Asturias de posguerra. 35\n\n \n\nMariam Nadirashvili, Natali Safi \u0026 Paige Ryan (University of California, Riverside)\n\nLanguage Policy and Linguistic Self-Awareness of Asturian through Social Media. 36\n\n \n\nAlina-Viorela Prelipcean (Universitatea „Ștefan cel Mare” din Suceava)\n\nAsturias en La Regenta: radiografía de una sociedad en transformación. 38\n\n \n\nMiguel Rodríguez Monteavaro (Universidá d'Uviéu)\n\nNeutru de materia n'asturianu: nuevos enfoques a partir del ELCOA.. 39\n\n \n\nElba Rodal Graña (Conservatorio Superior de Música del Principado de Asturias)\n\nTendiendo puentes entre la academia y las músicas populares urbanas: simbiosis e hibridación en la obra de Marisa Valle Roso. 40\n\n \n\nPaquita Suárez Coalla (Borough of Manhattan Community College, CUNY), Mictian Carax (Borough of Manhattan Community College, CUNY) \u0026 Berta Piñán (Academia de la Llingua Asturiana)\n\nDos voces pa una mesma mirada: la versión n’inglés de la poesía de Berta Piñán. 41\n\n \n\nImanol Suárez Palma (University of Florida)\n\nReduplicación verbal de afirmación enfática en asturiano. 42\n\n \n\nMiriam Villazón Valbuena \u0026 José Luis Godínez Altamirano (University of California, Riverside)\n\nIdentidades en tránsito: La representación de lo asturiano y lo mexicano en Los hijos de don Venancio. 44\n\n \n\nComité de organización. 46\n\n \n\nComité científico. 47\n\n \n\nJurado Premio Ana Cano  48"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19354303","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2026-04-20T16:28:48Z","registered":"2026-04-20T16:28:49Z","published":null,"updated":"2026-09-13T02:00:01Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19354304","type":"dois","attributes":{"doi":"10.5281/zenodo.19354304","identifiers":[{"identifier":"oai:zenodo.org:19354304","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/035pkj773","name":"Ştefan cel Mare University of 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Valbuena\n\n \n\n \n\n \n\nÍndice\n\n \n\n \n\nHéctor Álvarez Mella (Observatorio Global del Español, Instituto Cervantes)\n\nConferencia plenaria: Actitudes lingüísticas, migración e identidad en Asturias: ¿juego de espejos o reajuste axiológico?. 5\n\n \n\nAramo Álvarez (University of California, Davis),  Cristina Bleorțu (Universitatea „Ștefan cel Mare” din Suceava), Daniela Hăisan (Universitatea „Ștefan cel Mare” din Suceava), Miriam Villazón Valbuena (University of California, Riverside), \u0026 Silviu Popescu \n\nMesa redonda: Linguistics 4.0: How AI is transforming the study of language. 7\n\n \n\nIngrid Cobos López (Universidad de Córdoba), Miguel Cuevas Alonso (Universidade de Vigo), Covadonga Lamar Prieto (University of California, Riverside), Katia Rolán González (Universidade de Vigo), Vanesa Rodríguez Tembrás (Universität Heidelberg), \u0026 María Vila Tarela (Universidade de Vigo)\n\nMesa redonda: Humanidades médicas y lenguas minorizadas. 9\n\n \n\nEnrique Álvarez (Florida State University), Isabel Álvarez Sancho (Oklahoma State University), Eva Álvarez Vázquez (University of Massachusetts - Amherst), Luke Bowe (Kenyon Colleg), Nuria Godón (Florida Atlantic University), Laura Lesta (Middlebury College), Alfredo Martínez Expósito (University of Melbourne), \u0026 Bill Nichols (University of Texas at Dallas)\n\nMesa redonda: Amigos/as de los estudios asturianos (Primera edición dedicada al Prof. José Colmeiro, Universidad de Auckland) 11\n\n \n\nElena Álvarez Rodríguez (Universidá d'Uviéu)\n\nNueves formes d’espresión nes llingües minorizaes en relación col casu asturianu. 13\n\n \n\nIsabel Álvarez Sancho (Oklahoma State University)\n\nImaginaciones exílicas en la cultura asturiana contemporánea: creación, desplazamiento y reescritura de la historia. 14\n\n \n\nEva Álvarez-Vázquez (University of Massachusetts, Amherst)\n\nEcologías cuir en el paisaje posindustrial asturiano: «Materia Frágil, Becoming Inmortal» de Kela Coto. 16\n\n \n\nPaul Cahill (Pomona College)\n\n«Un punto ciego en el mapa conocido como la tierra de Nonú»: Imagining Asturias in the Poetry of Laura Ramos. 17\n\n \n\nFrancisco Calvo del Olmo (Ludwig-Maximilians-Universität München) \u0026 Gonzalo Llamedo-Pandiella (Universidá d'Uviéu)\n\nLa intercomprensión románica como vía pal aprendizaxe y la normalización del asturianu y el mirandés. 19\n\n \n\nErik Ekman (Oklahoma State University)\n\nAsturias in the works of Alfonso X.. 21\n\n \n\nAlba García Rodríguez (Universidad Complutense de Madrid)\n\nSociedad, lengua y cultura asturianas en la literatura actual: Aitana Castaño y Alfonso Zapico. 22\n\n \n\nLuis González Fernández (École des Hautes Études Hispaniques et Ibériques-Casa de Velázquez)\n\n‘Me pusi la permanente, creyendo que era más guapa’: coplas tradicionales y modas en el peinar 23\n\n \n\nAlba González Sanz (Universidad de Oviedo)\n\nRedes transhistóricas e hibridez lingüística en la obra literaria de Paquita Suárez Coalla. 25\n\n \n\nLavinia Ienceanu (Universitatea „Ștefan cel Mare” din Suceava)\n\nDe «Bovary española» a figura de pura estirpe asturiana: una reaproximación a La Regenta. 26\n\n \n\nIsabel María Kentengian Osorio (The College of New Jersey)\n\nLos paisajes lingüísticos de los museos etnográficos de Asturias. 28\n\n \n\nCovadonga Lamar Prieto (University of California, Riverside)\n\n«Salutate Mariam»: identidad asturiana, reconquista y devoción mariana en el sermón del Día de Covadonga (Madrid, 1793) 29\n\n \n\nAdrián Martínez Expósito (Universidá d'Uviéu)\n\nAnálisis redaccional y propuesta d'aparatu críticu de Filoloxía d'autor de los Poemes y Traducciones Xaponeses n'Asturianu de Fernán-Coronas. 31\n\n \n\nRafael Maldonado de Guevara Delgado (Universidad Carlos III)\n\nAsturias y la resistencia lingüística y cultural de Puerto Rico. 32\n\n \n\nLlucía Menéndez Díaz (Universidá d'Uviéu)\n\nEl gatu na fraseoloxía asturiana: análisis semántico-cognitivu. 33\n\n \n\nMariam Nadirashvili (University of California, Riverside)\n\nMemoria como resistencia: mujeres en la Asturias de posguerra. 35\n\n \n\nMariam Nadirashvili, Natali Safi \u0026 Paige Ryan (University of California, Riverside)\n\nLanguage Policy and Linguistic Self-Awareness of Asturian through Social Media. 36\n\n \n\nAlina-Viorela Prelipcean (Universitatea „Ștefan cel Mare” din Suceava)\n\nAsturias en La Regenta: radiografía de una sociedad en transformación. 38\n\n \n\nMiguel Rodríguez Monteavaro (Universidá d'Uviéu)\n\nNeutru de materia n'asturianu: nuevos enfoques a partir del ELCOA.. 39\n\n \n\nElba Rodal Graña (Conservatorio Superior de Música del Principado de Asturias)\n\nTendiendo puentes entre la academia y las músicas populares urbanas: simbiosis e hibridación en la obra de Marisa Valle Roso. 40\n\n \n\nPaquita Suárez Coalla (Borough of Manhattan Community College, CUNY), Mictian Carax (Borough of Manhattan Community College, CUNY) \u0026 Berta Piñán (Academia de la Llingua Asturiana)\n\nDos voces pa una mesma 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The 77 columns include trial metadata (model_key, condition, assortment_id, paraphrase_idx), the five-product display, the model's verbatim response, the extracted choice letter, the optimal letter and specification-optimal flag, and matched-model judge labels (judge_coherence, judge_specification_acknowledgment, judge_brand_reasoning).\nSHA-256 hashes are recorded in hashes.json in the GitHub repository for integrity verification.\nVersion 2 adds the curated bundle (spec-resistance-bundle-v2 zip): the manuscript sources and builds, the analysis and experiment code, the derived results and figures, the anonymised data and instruments of the four consumer studies and of the human-advisor study, the anonymised crowd-rater data, the consent form, and additional-comparisons/ with the script that recomputes every number in the paper. Human-study data are available for peer review and academic replication; code and model data are released under CC BY 4.0. Version 1's product dataset is unchanged."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.22727166","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-09-12T15:26:58Z","registered":"2026-09-12T15:26:59Z","published":null,"updated":"2026-09-12T15:28:37Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.16747595","type":"dois","attributes":{"doi":"10.5281/zenodo.16747595","identifiers":[],"creators":[{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/02grkyz14","name":"Western University","affiliationIdentifierScheme":"ROR"}],"givenName":"Yuki","familyName":"Bao","name":"Bao, Yuki","nameIdentifiers":[]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/01g9vbr38","name":"Oklahoma State University","affiliationIdentifierScheme":"ROR"}],"givenName":"Jefferson","familyName":"Frisbee","name":"Frisbee, Jefferson","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0003-2751-0599"}]},{"nameType":"Personal","affiliation":[{"affiliationIdentifier":"https://ror.org/02grkyz14","name":"Western University","affiliationIdentifierScheme":"ROR"}],"givenName":"Daniel","familyName":"Goldman","name":"Goldman, Daniel","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-8707-5536"}]}],"titles":[{"title":"AN ALGORITHM FOR GENERATING BIOPHYSICALLY REALISTIC THREE-DIMENSIONAL ARTERIOLAR NETWORKS APPLIED TO RAT SKELETAL MUSCLE"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[{"subject":"Microcirculation","subjectScheme":"MeSH"},{"subject":"arteriolar network"},{"subject":"computational modeling"},{"subject":"perfusion distribution"},{"subject":"biosimulation"},{"subject":"geometry"},{"subject":"topology"},{"subject":"fractal"},{"subject":"Hemodynamics","subjectScheme":"MeSH"}],"contributors":[],"dates":[{"date":"2025-11","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"SoftwareSourceCode","resourceTypeGeneral":"Software","citeproc":"article","bibtex":"misc","ris":"COMP","resourceType":""},"relatedIdentifiers":[{"relationType":"Continues","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.1152/japplphysiol.00896.2023","relatedIdentifierType":"DOI"},{"relationType":"IsPublishedIn","resourceTypeGeneral":"JournalArticle","relatedIdentifier":"10.14814/phy2.70704","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.16747596","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.17634926","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"},{"rightsIdentifierScheme":"SPDX","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rights":"MIT License","rightsIdentifier":"mit"}],"descriptions":[{"descriptionType":"Abstract","description":"AN ALGORITHM FOR GENERATING BIOPHYSICALLY REALISTIC THREE-DIMENSIONAL ARTERIOLAR NETWORKS APPLIED TO RAT SKELETAL MUSCLE (Version v3.0.0)\n\nDOI: https://doi.org/10.5281/zenodo.17634926Code license: MIT • Docs: CC BY 4.0\n\nRequirements:MATLAB R2019+; Toolboxes: Parallel Computing.\n\nQuick start (in MATLAB):cd 'code' % From the project root, set code as the current working directorymain; % Run the main script, main.m\n\nOutputs are written to ./results/ as file 'export.mat'\n\nCite:Bao, Y.; Goldman, D.; Frisbee, J. C. (2025). AN ALGORITHM FOR GENERATING BIOPHYSICALLY REALISTIC THREE-DIMENSIONAL ARTERIOLAR NETWORKS APPLIED TO RAT SKELETAL MUSCLE v3.0.0. 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