{"data":{"id":"10.5061/dryad.2jr4g","type":"dois","attributes":{"doi":"10.5061/dryad.2jr4g","prefix":"10.5061","suffix":"dryad.2jr4g","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Seekell, David A.","nameType":"Personal","givenName":"David A.","familyName":"Seekell","affiliation":[],"nameIdentifiers":[]},{"name":"Carpenter, Stephen R","nameType":"Personal","givenName":"Stephen R","familyName":"Carpenter","affiliation":[],"nameIdentifiers":[]},{"name":"Pace, Michael L","nameType":"Personal","givenName":"Michael L","familyName":"Pace","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Conditional heteroskedasticity as a leading indicator of ecological regime shifts"}],"publisher":"Dryad","container":{},"publicationYear":2011,"subjects":[{"subject":"computer simulations"},{"subject":"Time series analysis","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Statistical ecology"},{"subject":"Environmental variability"},{"subject":"Statistics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Food web theory"},{"subject":"Ecosystem ecology"}],"contributors":[],"dates":[{"date":"2011-06-03T23:09:37Z","dateType":"Submitted"},{"date":"2011-06-06T17:35:38Z","dateType":"Issued"},{"date":"2011-06-06T17:35:38Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1086/661898","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["392629 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Regime shifts are massive, often irreversible, re-arrangements of\n non-linear ecological processes that occur when systems pass critical\n transition points. Ecological regime shifts sometimes have severe\n consequences for human well-being including eutrophication in lakes,\n desertification, and species extinctions. Theoretical and laboratory\n evidence suggests that statistical anomalies may be detectable leading\n indicators of regime shifts in ecological time series, making it possible\n to foresee and potentially avert incipient regime shifts. Conditional\n heteroskedasticity is persistent variance which is characteristic of time\n series with clustered volatility. Here, we analyze conditional\n heteroskedasticity as a potential leading indicator of regime shifts in\n ecological time series. We evaluate conditional heteroskedasticity using\n ecological models with and without four types of critical transition. On\n approaching transition points, all time series contain significant\n conditional heteroskedasticity. This signal is detected hundreds of time\n steps in advance of the regime shift. Time series without regime shifts do\n not have significant conditional heteroskedasticity. Because probability\n values are easily associated with tests for conditional\n heteroskedasticity, detection of false positives in time series without\n regime shifts is minimized. This property reduces the need for a reference\n system to compare with the perturbed system.","descriptionType":"Abstract"},{"description":"Seekell_etal_DataData from the Seekell et al. conditional heteroskedasticity analysis of four stochastic ecosystem models, each with and without a regime shift. The data are provided in tables and parameter descriptions are in the table legends. Details, equations, and references for the models are in the main text of the paper.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"xml":"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHJlc291cmNlIHhtbG5zOnhzaT0iaHR0cDovL3d3dy53My5vcmcvMjAwMS9YTUxTY2hlbWEtaW5zdGFuY2UiIHhtbG5zPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCIgeHNpOnNjaGVtYUxvY2F0aW9uPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCBodHRwOi8vc2NoZW1hLmRhdGFjaXRlLm9yZy9tZXRhL2tlcm5lbC00L21ldGFkYXRhLnhzZCI+CiAgPGlkZW50aWZpZXIgaWRlbnRpZmllclR5cGU9IkRPSSI+MTAuNTA2MS9EUllBRC4ySlI0RzwvaWRlbnRpZmllcj4KICA8Y3JlYXRvcnM+CiAgICA8Y3JlYXRvcj4KICAgICAgPGNyZWF0b3JOYW1lIG5hbWVUeXBlPSJQZXJzb25hbCI+U2Vla2VsbCwgRGF2aWQgQS48L2NyZWF0b3JOYW1lPgogICAgPC9jcmVhdG9yPgogICAgPGNyZWF0b3I+CiAgICAgIDxjcmVhdG9yTmFtZSBuYW1lVHlwZT0iUGVyc29uYWwiPkNhcnBlbnRlciwgU3RlcGhlbiBSPC9jcmVhdG9yTmFtZT4KICAgIDwvY3JlYXRvcj4KICAgIDxjcmVhdG9yPgogICAgICA8Y3JlYXRvck5hbWUgbmFtZVR5cGU9IlBlcnNvbmFsIj5QYWNlLCBNaWNoYWVsIEw8L2NyZWF0b3JOYW1lPgogICAgPC9jcmVhdG9yPgogIDwvY3JlYXRvcnM+CiAgPHRpdGxlcz4KICAgIDx0aXRsZT4KICAgICAgRGF0YSBmcm9tOiBDb25kaXRpb25hbCBoZXRlcm9za2VkYXN0aWNpdHkgYXMgYSBsZWFkaW5nIGluZGljYXRvciBvZgogICAgICBlY29sb2dpY2FsIHJlZ2ltZSBzaGlmdHMKICAgIDwvdGl0bGU+CiAgPC90aXRsZXM+CiAgPHB1Ymxpc2hlciBwdWJsaXNoZXJJZGVudGlmaWVyPSJodHRwczovL3Jvci5vcmcvMDB4Nmg1bjk1IiBwdWJsaXNoZXJJZGVudGlmaWVyU2NoZW1lPSJST1IiIHNjaGVtZVVSST0iaHR0cHM6Ly9yb3Iub3JnLyI+RHJ5YWQ8L3B1Ymxpc2hlcj4KICA8cmVzb3VyY2VUeXBlIHJlc291cmNlVHlwZUdlbmVyYWw9IkRhdGFzZXQiPmRhdGFzZXQ8L3Jlc291cmNlVHlwZT4KICA8cHVibGljYXRpb25ZZWFyPjIwMTE8L3B1YmxpY2F0aW9uWWVhcj4KICA8c3ViamVjdHM+CiAgICA8c3ViamVjdD5jb21wdXRlciBzaW11bGF0aW9uczwvc3ViamVjdD4KICAgIDxzdWJqZWN0IHN1YmplY3RTY2hlbWU9IlBMT1MgU3ViamVjdCBBcmVhIFRoZXNhdXJ1cyIgc2NoZW1lVVJJPSJodHRwczovL2dpdGh1Yi5jb20vUExPUy9wbG9zLXRoZXNhdXJ1cyI+VGltZSBzZXJpZXMgYW5hbHlzaXM8L3N1YmplY3Q+CiAgICA8c3ViamVjdD5TdGF0aXN0aWNhbCBlY29sb2d5PC9zdWJqZWN0PgogICAgPHN1YmplY3Q+RW52aXJvbm1lbnRhbCB2YXJpYWJpbGl0eTwvc3ViamVjdD4KICAgIDxzdWJqZWN0IHN1YmplY3RTY2hlbWU9IlBMT1MgU3ViamVjdCBBcmVhIFRoZXNhdXJ1cyIgc2NoZW1lVVJJPSJodHRwczovL2dpdGh1Yi5jb20vUExPUy9wbG9zLXRoZXNhdXJ1cyI+U3RhdGlzdGljczwvc3ViamVjdD4KICAgIDxzdWJqZWN0PkZvb2Qgd2ViIHRoZW9yeTwvc3ViamVjdD4KICAgIDxzdWJqZWN0PkVjb3N5c3RlbSBlY29sb2d5PC9zdWJqZWN0PgogIDwvc3ViamVjdHM+CiAgPGRhdGVzPgogICAgPGRhdGUgZGF0ZVR5cGU9IlN1Ym1pdHRlZCI+MjAxMS0wNi0wM1QyMzowOTozN1o8L2RhdGU+CiAgICA8ZGF0ZSBkYXRlVHlwZT0iSXNzdWVkIj4yMDExLTA2LTA2VDE3OjM1OjM4WjwvZGF0ZT4KICAgIDxkYXRlIGRhdGVUeXBlPSJBdmFpbGFibGUiPjIwMTEtMDYtMDZUMTc6MzU6MzhaPC9kYXRlPgogIDwvZGF0ZXM+CiAgPGxhbmd1YWdlPmVuPC9sYW5ndWFnZT4KICA8cmVsYXRlZElkZW50aWZpZXJzPgogICAgPHJlbGF0ZWRJZGVudGlmaWVyIHJlbGF0aW9uVHlwZT0iSXNDaXRlZEJ5IiByZWxhdGVkSWRlbnRpZmllclR5cGU9IkRPSSI+aHR0cHM6Ly9kb2kub3JnLzEwLjEwODYvNjYxODk4PC9yZWxhdGVkSWRlbnRpZmllcj4KICA8L3JlbGF0ZWRJZGVudGlmaWVycz4KICA8c2l6ZXM+CiAgICA8c2l6ZT4zOTI2MjkgYnl0ZXM8L3NpemU+CiAgPC9zaXplcz4KICA8dmVyc2lvbj4xPC92ZXJzaW9uPgogIDxyaWdodHNMaXN0PgogICAgPHJpZ2h0cyByaWdodHNVUkk9Imh0dHBzOi8vc3BkeC5vcmcvbGljZW5zZXMvQ0MwLTEuMC5odG1sIj5DcmVhdGl2ZSBDb21tb25zIFplcm8gdjEuMCBVbml2ZXJzYWw8L3JpZ2h0cz4KICA8L3JpZ2h0c0xpc3Q+CiAgPGRlc2NyaXB0aW9ucz4KICAgIDxkZXNjcmlwdGlvbiBkZXNjcmlwdGlvblR5cGU9IkFic3RyYWN0Ij4KICAgICAgUmVnaW1lIHNoaWZ0cyBhcmUgbWFzc2l2ZSwgb2Z0ZW4gaXJyZXZlcnNpYmxlLCByZS1hcnJhbmdlbWVudHMgb2YKICAgICAgbm9uLWxpbmVhciBlY29sb2dpY2FsIHByb2Nlc3NlcyB0aGF0IG9jY3VyIHdoZW4gc3lzdGVtcyBwYXNzIGNyaXRpY2FsCiAgICAgIHRyYW5zaXRpb24gcG9pbnRzLiBFY29sb2dpY2FsIHJlZ2ltZSBzaGlmdHMgc29tZXRpbWVzIGhhdmUgc2V2ZXJlCiAgICAgIGNvbnNlcXVlbmNlcyBmb3IgaHVtYW4gd2VsbC1iZWluZyBpbmNsdWRpbmcgZXV0cm9waGljYXRpb24gaW4gbGFrZXMsCiAgICAgIGRlc2VydGlmaWNhdGlvbiwgYW5kIHNwZWNpZXMgZXh0aW5jdGlvbnMuIFRoZW9yZXRpY2FsIGFuZCBsYWJvcmF0b3J5CiAgICAgIGV2aWRlbmNlIHN1Z2dlc3RzIHRoYXQgc3RhdGlzdGljYWwgYW5vbWFsaWVzIG1heSBiZSBkZXRlY3RhYmxlIGxlYWRpbmcKICAgICAgaW5kaWNhdG9ycyBvZiByZWdpbWUgc2hpZnRzIGluIGVjb2xvZ2ljYWwgdGltZSBzZXJpZXMsIG1ha2luZyBpdCBwb3NzaWJsZQogICAgICB0byBmb3Jlc2VlIGFuZCBwb3RlbnRpYWxseSBhdmVydCBpbmNpcGllbnQgcmVnaW1lIHNoaWZ0cy4gQ29uZGl0aW9uYWwKICAgICAgaGV0ZXJvc2tlZGFzdGljaXR5IGlzIHBlcnNpc3RlbnQgdmFyaWFuY2Ugd2hpY2ggaXMgY2hhcmFjdGVyaXN0aWMgb2YgdGltZQogICAgICBzZXJpZXMgd2l0aCBjbHVzdGVyZWQgdm9sYXRpbGl0eS4gSGVyZSwgd2UgYW5hbHl6ZSBjb25kaXRpb25hbAogICAgICBoZXRlcm9za2VkYXN0aWNpdHkgYXMgYSBwb3RlbnRpYWwgbGVhZGluZyBpbmRpY2F0b3Igb2YgcmVnaW1lIHNoaWZ0cyBpbgogICAgICBlY29sb2dpY2FsIHRpbWUgc2VyaWVzLiBXZSBldmFsdWF0ZSBjb25kaXRpb25hbCBoZXRlcm9za2VkYXN0aWNpdHkgdXNpbmcKICAgICAgZWNvbG9naWNhbCBtb2RlbHMgd2l0aCBhbmQgd2l0aG91dCBmb3VyIHR5cGVzIG9mIGNyaXRpY2FsIHRyYW5zaXRpb24uIE9uCiAgICAgIGFwcHJvYWNoaW5nIHRyYW5zaXRpb24gcG9pbnRzLCBhbGwgdGltZSBzZXJpZXMgY29udGFpbiBzaWduaWZpY2FudAogICAgICBjb25kaXRpb25hbCBoZXRlcm9za2VkYXN0aWNpdHkuIFRoaXMgc2lnbmFsIGlzIGRldGVjdGVkIGh1bmRyZWRzIG9mIHRpbWUKICAgICAgc3RlcHMgaW4gYWR2YW5jZSBvZiB0aGUgcmVnaW1lIHNoaWZ0LiBUaW1lIHNlcmllcyB3aXRob3V0IHJlZ2ltZSBzaGlmdHMgZG8KICAgICAgbm90IGhhdmUgc2lnbmlmaWNhbnQgY29uZGl0aW9uYWwgaGV0ZXJvc2tlZGFzdGljaXR5LiBCZWNhdXNlIHByb2JhYmlsaXR5CiAgICAgIHZhbHVlcyBhcmUgZWFzaWx5IGFzc29jaWF0ZWQgd2l0aCB0ZXN0cyBmb3IgY29uZGl0aW9uYWwKICAgICAgaGV0ZXJvc2tlZGFzdGljaXR5LCBkZXRlY3Rpb24gb2YgZmFsc2UgcG9zaXRpdmVzIGluIHRpbWUgc2VyaWVzIHdpdGhvdXQKICAgICAg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