[ { "key": "JK4W33K2", "version": 34, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/JK4W33K2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/JK4W33K2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Phillips and Dudík", "parsedDate": "2008", "numChildren": 0 }, "data": { "key": "JK4W33K2", "version": 34, "itemType": "journalArticle", "title": "Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation", "creators": [ { "creatorType": "author", "firstName": "Steven J.", "lastName": "Phillips" }, { "creatorType": "author", "firstName": "Miroslav", "lastName": "Dudík" } ], "abstractNote": "Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time-consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use “default settings”, tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence-only data. We evaluate our method on independently collected high-quality presence-absence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce “hinge features” that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore “background sampling” strategies that cope with sample selection bias and decrease model-building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence-only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) “target-group” background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance.", "publicationTitle": "Ecography", "publisher": "", "place": "", "date": "2008", "volume": "31", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "161-175", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.0906-7590.2008.5203.x", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/j.0906-7590.2008.5203.x/abstract", "accessDate": "2017-12-05T00:07:23Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2017-12-06T05:36:58Z", "dateModified": "2017-12-08T04:21:06Z" } }, { "key": "4H6MCED6", "version": 31, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/4H6MCED6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/4H6MCED6", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Radosavljevic and Anderson", "parsedDate": "2014-04-01", "numChildren": 0 }, "data": { "key": "4H6MCED6", "version": 31, "itemType": "journalArticle", "title": "Making better Maxent models of species distributions: complexity, overfitting and evaluation", "creators": [ { "creatorType": "author", "firstName": "Aleksandar", "lastName": "Radosavljevic" }, { "creatorType": "author", "firstName": "Robert P.", "lastName": "Anderson" } ], "abstractNote": "Aim\n\nModels of species niches and distributions have become invaluable to biogeographers over the past decade, yet several outstanding methodological issues remain. Here we address three critical ones: selecting appropriate evaluation data, detecting overfitting, and tuning program settings to approximate optimal model complexity. We integrate solutions to these issues for Maxent models, using the Caribbean spiny pocket mouse, Heteromys anomalus, as an example.\n\n\nLocation\n\nNorth-western South America.\n\n\nMethods\n\nWe partitioned data into calibration and evaluation datasets via three variations of k-fold cross-validation: randomly partitioned, geographically structured and masked geographically structured (which restricts background data to regions corresponding to calibration localities). Then, we carried out tuning experiments by varying the level of regularization, which controls model complexity. Finally, we gauged performance by quantifying discriminatory ability and overfitting, as well as via visual inspections of maps of the predictions in geography.\n\n\nResults\n\nPerformance varied among data-partitioning approaches and among regularization multipliers. The randomly partitioned approach inflated estimates of model performance and the geographically structured approach showed high overfitting. In contrast, the masked geographically structured approach allowed selection of high-performing models based on all criteria. Discriminatory ability showed a slight peak in performance around the default regularization multiplier. However, regularization levels two to four times higher than the default yielded substantially lower overfitting. Visual inspection of maps of model predictions coincided with the quantitative evaluations.\n\n\nMain conclusions\n\nSpecies-specific tuning of model parameters can improve the performance of Maxent models. Further, accurate estimates of model performance and overfitting depend on using independent evaluation data. These strategies for model evaluation may be useful for other modelling methods as well.", "publicationTitle": "Journal of Biogeography", "publisher": "", "place": "", "date": "April 1, 2014", "volume": "41", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "629-643", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "J. Biogeogr.", "DOI": "10.1111/jbi.12227", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/jbi.12227/abstract", "accessDate": "2017-12-07T00:05:22Z", "PMID": "", "PMCID": "", "ISSN": "1365-2699", "archive": "", "archiveLocation": "", "shortTitle": "Making better Maxent models of species distributions", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Cross validation", "type": 1 }, { "tag": "Heteromys", "type": 1 }, { "tag": "Maxent", "type": 1 }, { "tag": "South America", "type": 1 }, { "tag": "evaluation", "type": 1 }, { "tag": "niche", "type": 1 }, { "tag": "overfitting", "type": 1 }, { "tag": "rodent", "type": 1 }, { "tag": "smoothing", "type": 1 }, { "tag": "tuning", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2017-12-08T04:17:55Z", "dateModified": "2017-12-08T04:17:55Z" } }, { "key": "FB6M9GEH", "version": 26, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/FB6M9GEH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/FB6M9GEH", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "McCormack et al.", "parsedDate": "2010", "numChildren": 0 }, "data": { "key": "FB6M9GEH", "version": 26, "itemType": "journalArticle", "title": "Does Niche Divergence Accompany Allopatric Divergence In Aphelocoma Jays As Predicted Under Ecological Speciation?: Insights From Tests With Niche Models", "creators": [ { "creatorType": "author", "firstName": "John E.", "lastName": "McCormack" }, { "creatorType": "author", "firstName": "Amanda J.", "lastName": "Zellmer" }, { "creatorType": "author", "firstName": "L. Lacey", "lastName": "Knowles" } ], "abstractNote": "The role of ecology in the origin of species has been the subject of long-standing interest to evolutionary biologists. New sources of spatially explicit ecological data allow for large-scale tests of whether speciation is associated with niche divergence or whether closely related species tend to be similar ecologically (niche conservatism). Because of the confounding effects of spatial autocorrelation of environmental variables, we generate null expectations for niche divergence for both an ecological-niche modeling and a multivariate approach to address the question: do allopatrically distributed taxa occupy similar niches? In a classic system for the study of niche evolution2014the Aphelocoma jays2014we show that there is little evidence for niche divergence among Mexican Jay (A. ultramarina) lineages in the process of speciation, contrary to previous results. In contrast, Aphelocoma species that exist in partial sympatry in some regions show evidence for niche divergence. Our approach is widely applicable to the many cases of allopatric lineages in the beginning stages of speciation. These results do not support an ecological speciation model for Mexican Jay lineages because, in most cases, the allopatric environments they occupy are not significantly more divergent than expected under a null model.", "publicationTitle": "Evolution", "publisher": "", "place": "", "date": "2010", "volume": "64", "issue": "5", "section": "", "partNumber": "", "partTitle": "", "pages": "1231-1244", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.1558-5646.2009.00900.x", "citationKey": "", "url": "http://dx.doi.org/10.1111/j.1558-5646.2009.00900.x", "accessDate": "2010-05-07T15:24:47Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "DOES NICHE DIVERGENCE ACCOMPANY ALLOPATRIC DIVERGENCE IN APHELOCOMA JAYS AS PREDICTED UNDER ECOLOGICAL SPECIATION?", "language": "", "libraryCatalog": "Wiley InterScience", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Aphelocoma" }, { "tag": "bird" }, { "tag": "niche conservatism" } ], "collections": [], "relations": {}, "dateAdded": "2016-09-24T19:09:52Z", "dateModified": "2017-12-05T06:09:26Z" } }, { "key": "TSBZWJGE", "version": 25, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/TSBZWJGE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/TSBZWJGE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Phillips", "parsedDate": "2007", "numChildren": 0 }, "data": { "key": "TSBZWJGE", "version": 25, "itemType": "journalArticle", "title": "Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al. (2007)", "creators": [ { "creatorType": "author", "firstName": "Steven J.", "lastName": "Phillips" } ], "abstractNote": "", "publicationTitle": "Ecography", "publisher": "", "place": "", "date": "2007", "volume": "31", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "272-278", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://www3.interscience.wiley.com/cgi-bin/fulltext/119407305/PDFSTART", "accessDate": "2009-11-12T05:58:13Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "method" } ], "collections": [], "relations": {}, "dateAdded": "2015-04-28T03:42:45Z", "dateModified": "2017-12-05T05:54:05Z" } }, { "key": "M8ABUAFV", "version": 29, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/M8ABUAFV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/M8ABUAFV", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Peterson", "parsedDate": "2006-12-01", "numChildren": 0 }, "data": { "key": "M8ABUAFV", "version": 29, "itemType": "journalArticle", "title": "Uses and Requirements of Ecological Niche Models and Related Distributional Models", "creators": [ { "creatorType": "author", "firstName": "A. Townsend", "lastName": "Peterson" } ], "abstractNote": "", "publicationTitle": "Biodiversity Informatics", "publisher": "", "place": "", "date": "2006/12/01", "volume": "3", "issue": "0", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "https://journals.ku.edu/jbi/article/view/29", "accessDate": "2017-12-05T00:33:12Z", "PMID": "", "PMCID": "", "ISSN": "15469735", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "journals.ku.edu", "callNumber": "", "rights": "Copyright (c) 2006 A. Townsend Peterson", "extra": "", "tags": [ { "tag": "Ecological niche", "type": 1 }, { "tag": "distribution modeling", "type": 1 }, { "tag": "ecological niche modeling", "type": 1 }, { "tag": "geographic distributions", "type": 1 }, { "tag": "method" } ], "collections": [], "relations": {}, "dateAdded": "2017-12-05T05:53:08Z", "dateModified": "2017-12-05T05:53:08Z" } }, { "key": "GWFTC4UX", "version": 22, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/GWFTC4UX", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/GWFTC4UX", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Graham et al.", "parsedDate": "2004-08-01", "numChildren": 2 }, "data": { "key": "GWFTC4UX", "version": 22, "itemType": "journalArticle", "title": "Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs", "creators": [ { "creatorType": "author", "firstName": "Catherine H.", "lastName": "Graham" }, { "creatorType": "author", "firstName": "Santiago R.", "lastName": "Ron" }, { "creatorType": "author", "firstName": "Juan C.", "lastName": "Santos" }, { "creatorType": "author", "firstName": "Christopher J.", "lastName": "Schneider" }, { "creatorType": "author", "firstName": "Craig", "lastName": "Moritz" }, { "creatorType": "author", "firstName": "C.", "lastName": "Cunningham" } ], "abstractNote": "We developed an approach that combines distribution data, environmental geographic information system layers, environmental niche models, and phylogenetic information to investigate speciation processes. We used Ecuadorian frogs of the family Dendrobatidae to illustrate our methodology. For dendrobatids there are several cases for which there is significant environmental divergence for allopatric and parapatric lineages. The consistent pattern that many related taxa or nodes exist in distinct environmental space reinforces Lynch and Duellman's hypothesis that differential selection likely played an important role in species differentiation of frogs in the Andes. There is also some evidence that the Río Esmeraldas basin is a geographic barrier to species distributed in low to middle elevations on the western side of the Andes. Another useful aspect of this approach is that it can point to common environmental parameters that correlate with speciation. For dendrobatids, sister clades generally segregate along temperature/elevational and/or seasonality axes. The joint analysis of environmental and geographic data for this group of dendrobatid frogs has identified potentially important speciation mechanisms and specific sister lineages that warrant intensive study to test hypotheses generated in this investigation. Further, the method outlined in this paper will be increasingly useful as knowledge of distribution and phylogeny of tropical species increases.", "publicationTitle": "Evolution", "publisher": "", "place": "", "date": "August 1, 2004", "volume": "58", "issue": "8", "section": "", "partNumber": "", "partTitle": "", "pages": "1781-1793", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Evolution", "DOI": "10.1554/03-274", "citationKey": "", "url": "http://www.bioone.org/doi/abs/10.1554/03-274", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0014-3820", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "bioone.org (Atypon)", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "niche conservatism" } ], "collections": [], "relations": {}, "dateAdded": "2017-11-23T20:45:38Z", "dateModified": "2017-11-23T20:45:38Z" } }, { "key": "FTN4B6SJ", "version": 19, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/FTN4B6SJ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/FTN4B6SJ", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/346853/items/GWFTC4UX", "type": "application/json" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } } }, "data": { "key": "FTN4B6SJ", "version": 19, "parentItem": "GWFTC4UX", "itemType": "attachment", "linkMode": "linked_url", "title": "Full Text PDF", "accessDate": "2017-11-23T20:45:38Z", "url": "http://www.bioone.org/doi/pdf/10.1554/03-274", "note": "", "contentType": "application/pdf", "charset": "", "tags": [], "relations": {}, "dateAdded": "2017-11-23T20:45:38Z", "dateModified": "2017-11-23T20:45:38Z" } }, { "key": "57R54GTD", "version": 19, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/57R54GTD", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/57R54GTD", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/346853/items/GWFTC4UX", "type": "application/json" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } } }, "data": { "key": "57R54GTD", "version": 19, "parentItem": "GWFTC4UX", "itemType": "attachment", "linkMode": "linked_url", "title": "Snapshot", "accessDate": "2017-11-23T20:45:38Z", "url": "http://www.bioone.org/doi/abs/10.1554/03-274", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2017-11-23T20:45:38Z", "dateModified": "2017-11-23T20:45:38Z" } }, { "key": "DBZURD3G", "version": 20, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/DBZURD3G", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/DBZURD3G", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Bradley and Fleishman", "parsedDate": "2008", "numChildren": 0 }, "data": { "key": "DBZURD3G", "version": 20, "itemType": "journalArticle", "title": "Can remote sensing of land cover improve species distribution modelling?", "creators": [ { "creatorType": "author", "firstName": "Bethany A.", "lastName": "Bradley" }, { "creatorType": "author", "firstName": "Erica", "lastName": "Fleishman" } ], "abstractNote": "", "publicationTitle": "Journal of Biogeography", "publisher": "", "place": "", "date": "2008", "volume": "35", "issue": "7", "section": "", "partNumber": "", "partTitle": "", "pages": "1158-1159", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.1365-2699.2008.01928.x", "citationKey": "", "url": "http://dx.doi.org/10.1111/j.1365-2699.2008.01928.x", "accessDate": "2009-09-04T05:20:01Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Wiley InterScience", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "method" } ], "collections": [], "relations": {}, "dateAdded": "2017-11-23T19:00:58Z", "dateModified": "2017-11-23T19:00:58Z" } }, { "key": "47A33KJH", "version": 13, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/47A33KJH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/47A33KJH", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/346853/items/CI8CJWA4", "type": "application/json" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } } }, "data": { "key": "47A33KJH", "version": 13, "parentItem": "CI8CJWA4", "itemType": "attachment", "linkMode": "linked_url", "title": "Full Text PDF", "accessDate": "2016-09-24T19:50:13Z", "url": "http://www.pnas.org/content/108/39/16322.full.pdf", "note": "", "contentType": "application/pdf", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-09-24T19:50:13Z", "dateModified": "2016-09-24T19:50:13Z" } }, { "key": "FTXPRIFI", "version": 13, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/FTXPRIFI", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/FTXPRIFI", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/346853/items/CI8CJWA4", "type": "application/json" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } } }, "data": { "key": "FTXPRIFI", "version": 13, "parentItem": "CI8CJWA4", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2016-09-24T19:50:13Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/21930900", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-09-24T19:50:13Z", "dateModified": "2016-09-24T19:50:13Z" } }, { "key": "6AHI4TU6", "version": 13, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/6AHI4TU6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/6AHI4TU6", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/346853/items/CI8CJWA4", "type": "application/json" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } } }, "data": { "key": "6AHI4TU6", "version": 13, "parentItem": "CI8CJWA4", "itemType": "attachment", "linkMode": "linked_url", "title": "Snapshot", "accessDate": "2016-09-24T19:50:13Z", "url": "http://www.pnas.org/content/108/39/16322.long", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-09-24T19:50:13Z", "dateModified": "2016-09-24T19:50:13Z" } }, { "key": "HA9PSBKW", "version": 12, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/HA9PSBKW", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/HA9PSBKW", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Heads", "parsedDate": "2015-06-01", "numChildren": 0 }, "data": { "key": "HA9PSBKW", "version": 12, "itemType": "journalArticle", "title": "The relationship between biogeography and ecology: envelopes, models, predictions", "creators": [ { "creatorType": "author", "firstName": "Michael", "lastName": "Heads" } ], "abstractNote": "This paper reviews ideas on the relationship between the ecology of clades and their distribution. Ecological biogeography represents a tradition that dates back to ancient times. It assumes that the distribution of organisms is explained by factors of present environment, especially climate. In contrast, modern systematics, following its origins in the Renaissance, concluded with Darwin that ‘neither the similarity nor the dissimilarity of the inhabitants of various regions can be accounted for by their climatal and other physical conditions’. In many cases, species distribution models – ecological niche models – based on the current environment of a species (its environmental envelope) fail to predict the actual distribution of the species. In particular, they often over-predict distributions. In addition, a group's niche often varies in space and time. These results provide valuable evidence that Darwin was correct, and many ecologists now recognise that there is a problem with the niche theory of distribution. Current ecological processes explain distribution at smaller scales than do biogeographical and evolutionary processes, but the latter can lead to patterns that are much more local than many ecologists have assumed. Biogeographical phenomena often occur at a much smaller scale than that of the Wallacean regions. In areas that have been subjected to marine inundation or intense tectonism, many centres of endemism are only tens of kilometres across. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, 115, 456–468.", "publicationTitle": "Biological Journal of the Linnean Society", "publisher": "", "place": "", "date": "June 1, 2015", "volume": "115", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "456-468", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Biol J Linn Soc Lond", "DOI": "10.1111/bij.12486", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/bij.12486/abstract", "accessDate": "2015-11-16T05:58:00Z", "PMID": "", "PMCID": "", "ISSN": "1095-8312", "archive": "", "archiveLocation": "", "shortTitle": "The relationship between biogeography and ecology", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2015 The Linnean Society of London", "extra": "", "tags": [ { "tag": "California", "type": 1 }, { "tag": "Climate", "type": 1 }, { "tag": "Grinnell", "type": 1 }, { "tag": "centre of origin", "type": 1 }, { "tag": "dispersal", "type": 1 }, { "tag": "niche", "type": 1 }, { "tag": "species distribution models", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-09-24T19:23:19Z", "dateModified": "2016-09-24T19:23:19Z" } }, { "key": "GSF4TG2B", "version": 22, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/GSF4TG2B", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/GSF4TG2B", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Liu et al.", "parsedDate": "2005", "numChildren": 0 }, "data": { "key": "GSF4TG2B", "version": 22, "itemType": "journalArticle", "title": "Selecting thresholds of occurrence in the prediction of species distributions", "creators": [ { "creatorType": "author", "firstName": "Canran", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Pam M.", "lastName": "Berry" }, { "creatorType": "author", "firstName": "Terence P.", "lastName": "Dawson" }, { "creatorType": "author", "firstName": "Richard G.", "lastName": "Pearson" } ], "abstractNote": "Transforming the results of species distribution modelling from probabilities of or suitabilities for species occurrence to presences/absences needs a specific threshold. Even though there are many approaches to determining thresholds, there is no comparative study. In this paper, twelve approaches were compared using two species in Europe and artificial neural networks, and the modelling results were assessed using four indices: sensitivity, specificity, overall prediction success and Cohen's kappa statistic. The results show that prevalence approach, average predicted probability/suitability approach, and three sensitivity-specificity-combined approaches, including sensitivity-specificity sum maximization approach, sensitivity-specificity equality approach and the approach based on the shortest distance to the top-left corner (0,1) in ROC plot, are the good ones. The commonly used kappa maximization approach is not as good as the afore-mentioned ones, and the fixed threshold approach is the worst one. We also recommend using datasets with prevalence of 50% to build models if possible since most optimization criteria might be satisfied or nearly satisfied at the same time, and therefore it's easier to find optimal thresholds in this situation.", "publicationTitle": "Ecography", "publisher": "", "place": "", "date": "2005", "volume": "28", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "385-393", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.0906-7590.2005.03957.x", "citationKey": "", "url": "http://dx.doi.org/10.1111/j.0906-7590.2005.03957.x", "accessDate": "2009-12-23T00:32:25Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Wiley InterScience", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "method" }, { "tag": "niche modelling" } ], "collections": [], "relations": {}, "dateAdded": "2016-09-24T19:15:23Z", "dateModified": "2016-09-24T19:15:23Z" } }, { "key": "8V3XN8A7", "version": 11, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/8V3XN8A7", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/8V3XN8A7", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "HIJMANS and GRAHAM", "parsedDate": "2006", "numChildren": 0 }, "data": { "key": "8V3XN8A7", "version": 11, "itemType": "journalArticle", "title": "The ability of climate envelope models to predict the effect of climate change on species distributions", "creators": [ { "creatorType": "author", "firstName": "ROBERT J.", "lastName": "HIJMANS" }, { "creatorType": "author", "firstName": "CATHERINE H.", "lastName": "GRAHAM" } ], "abstractNote": "Climate envelope models (CEMs) have been used to predict the distribution of species under current, past, and future climatic conditions by inferring a species' environmental requirements from localities where it is currently known to occur. CEMs can be evaluated for their ability to predict current species distributions but it is unclear whether models that are successful in predicting current distributions are equally successful in predicting distributions under different climates (i.e. different regions or time periods). We evaluated the ability of CEMs to predict species distributions under different climates by comparing their predictions with those obtained with a mechanistic model (MM). In an MM the distribution of a species is modeled based on knowledge of a species' physiology. The potential distributions of 100 plant species were modeled with an MM for current conditions, a past climate reconstruction (21 000 years before present) and a future climate projection (double preindustrial CO2 conditions). Point localities extracted from the currently suitable area according to the MM were used to predict current, future, and past distributions with four CEMs covering a broad range of statistical approaches: Bioclim (percentile distributions), Domain (distance metric), GAM (general additive modeling), and Maxent (maximum entropy). Domain performed very poorly, strongly underestimating range sizes for past or future conditions. Maxent and GAM performed as well under current climates as under past and future climates. Bioclim slightly underestimated range sizes but the predicted ranges overlapped more with the ranges predicted with the MM than those predicted with GAM did. Ranges predicted with Maxent overlapped most with those produced with the MMs, but compared with the ranges predicted with GAM they were more variable and sometimes much too large. Our results suggest that some CEMs can indeed be used to predict species distributions under climate change, but individual modeling approaches should be validated for this purpose, and model choice could be made dependent on the purpose of a particular study.", "publicationTitle": "Global Change Biology", "publisher": "", "place": "", "date": "2006", "volume": "12", "issue": "12", "section": "", "partNumber": "", "partTitle": "", "pages": "2272-2281", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.1365-2486.2006.01256.x", "citationKey": "", "url": "http://dx.doi.org/10.1111/j.1365-2486.2006.01256.x", "accessDate": "2010-01-08T23:32:51Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Wiley InterScience", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Climate Change" }, { "tag": "Species Distribution Modeling" } ], "collections": [], "relations": {}, "dateAdded": "2016-09-24T19:15:09Z", "dateModified": "2016-09-24T19:15:09Z" } }, { "key": "TIT6573X", "version": 27, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/TIT6573X", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/TIT6573X", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Elith et al.", "parsedDate": "2010-12-01", "numChildren": 0 }, "data": { "key": "TIT6573X", "version": 27, "itemType": "journalArticle", "title": "The art of modelling range‐shifting species", "creators": [ { "creatorType": "author", "firstName": "Jane", "lastName": "Elith" }, { "creatorType": "author", "firstName": "Michael", "lastName": "Kearney" }, { "creatorType": "author", "firstName": "Steven", "lastName": "Phillips" } ], "abstractNote": "1. Species are shifting their ranges at an unprecedented rate through human transportation and environmental change. Correlative species distribution models (SDMs) are frequently applied for predicting potential future distributions of range-shifting species, despite these models’ assumptions that species are at equilibrium with the environments used to train (fit) the models, and that the training data are representative of conditions to which the models are predicted. Here we explore modelling approaches that aim to minimize extrapolation errors and assess predictions against prior biological knowledge. Our aim was to promote methods appropriate to range-shifting species.", "publicationTitle": "Methods in Ecology and Evolution", "publisher": "", "place": "", "date": "2010/12/01", "volume": "1", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "330-342", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.2041-210X.2010.00036.x", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2010.00036.x/abstract", "accessDate": "2011-06-13T19:52:26Z", "PMID": "", "PMCID": "", "ISSN": "2041-210X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Climate Change", "type": 1 }, { "tag": "cane toad", "type": 1 }, { "tag": "changing correlations", "type": 1 }, { "tag": "extrapolation", "type": 1 }, { "tag": "invasive species", "type": 1 }, { "tag": "method" }, { "tag": "niche models", "type": 1 }, { "tag": "novel environments", "type": 1 }, { "tag": "species distribution models", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-09-24T19:14:52Z", "dateModified": "2016-09-24T19:14:52Z" } }, { "key": "IQMA4I3Z", "version": 21, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/IQMA4I3Z", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/IQMA4I3Z", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Elith et al.", "parsedDate": "2010", "numChildren": 0 }, "data": { "key": "IQMA4I3Z", "version": 21, "itemType": "journalArticle", "title": "A statistical explanation of MaxEnt for ecologists", "creators": [ { "creatorType": "author", "firstName": "Jane", "lastName": "Elith" }, { "creatorType": "author", "firstName": "Steven J.", "lastName": "Phillips" }, { "creatorType": "author", "firstName": "Trevor", "lastName": "Hastie" }, { "creatorType": "author", "firstName": "Miroslav", "lastName": "Dudík" }, { "creatorType": "author", "firstName": "Yung En", "lastName": "Chee" }, { "creatorType": "author", "firstName": "Colin J.", "lastName": "Yates" } ], "abstractNote": "", "publicationTitle": "Diversity and Distributions", "publisher": "", "place": "", "date": "11/2010", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "no-no", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.1472-4642.2010.00725.x", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/j.1472-4642.2010.00725.x/full", "accessDate": "2010-12-09T05:37:27Z", "PMID": "", "PMCID": "", "ISSN": "13669516", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Maximum entropy" }, { "tag": "method" } ], "collections": [], "relations": {}, "dateAdded": "2015-11-12T17:45:37Z", "dateModified": "2015-11-12T17:45:37Z" } }, { "key": "7WBZ8JQ7", "version": 23, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/7WBZ8JQ7", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/7WBZ8JQ7", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Soberón", "parsedDate": "2007-12-01", "numChildren": 0 }, "data": { "key": "7WBZ8JQ7", "version": 23, "itemType": "journalArticle", "title": "Grinnellian and Eltonian niches and geographic distributions of species", "creators": [ { "creatorType": "author", "firstName": "Jorge", "lastName": "Soberón" } ], "abstractNote": "In the recent past, availability of large data sets of species presences has increased by orders of magnitude. This, together with developments in geographical information systems and statistical methods, has enabled scientists to calculate, for thousands of species, the environmental conditions of their distributional areas. The profiles thus obtained are obviously related to niche concepts in the Grinnell tradition, and separated from those in Elton’s tradition. I argue that it is useful to define Grinnellian and Eltonian niches on the basis of the types of variables used to calculate them, the natural spatial scale at which they can be measured, and the dispersal of the individuals over the environment. I use set theory notation and analogies derived from population ecology theory to obtain formal definitions of areas of distribution and several types of niches. This brings clarity to several practical and fundamental questions in macroecology and biogeography.", "publicationTitle": "Ecology Letters", "publisher": "", "place": "", "date": "2007/12/01", "volume": "10", "issue": "12", "section": "", "partNumber": "", "partTitle": "", "pages": "1115-1123", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.1461-0248.2007.01107.x", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/j.1461-0248.2007.01107.x/full", "accessDate": "2012-03-08T20:06:12Z", "PMID": "", "PMCID": "", "ISSN": "1461-0248", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "onlinelibrary.wiley.com", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Bionomic", "type": 1 }, { "tag": "Elton", "type": 1 }, { "tag": "Grinnell", "type": 1 }, { "tag": "distributional areas", "type": 1 }, { "tag": "niche", "type": 1 }, { "tag": "niche modelling" }, { "tag": "scenopoetic", "type": 1 }, { "tag": "spatial scaling", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2015-11-12T17:45:23Z", "dateModified": "2015-11-12T17:45:23Z" } }, { "key": "62Z76RUC", "version": 28, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/62Z76RUC", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/62Z76RUC", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Lozier et al.", "parsedDate": "2009", "numChildren": 1 }, "data": { "key": "62Z76RUC", "version": 28, "itemType": "journalArticle", "title": "Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling", "creators": [ { "creatorType": "author", "firstName": "J. D.", "lastName": "Lozier" }, { "creatorType": "author", "firstName": "P.", "lastName": "Aniello" }, { "creatorType": "author", "firstName": "M. J.", "lastName": "Hickerson" } ], "abstractNote": "", "publicationTitle": "Journal of Biogeography", "publisher": "", "place": "", "date": "09/2009", "volume": "36", "issue": "9", "section": "", "partNumber": "", "partTitle": "", "pages": "1623-1627", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.1365-2699.2009.02152.x", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2699.2009.02152.x/abstract", "accessDate": "2011-03-23T18:30:02Z", "PMID": "", "PMCID": "", "ISSN": "03050270", "archive": "", "archiveLocation": "", "shortTitle": "Predicting the distribution of Sasquatch in western North America", "language": "", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Maximum entropy" }, { "tag": "method" } ], "collections": [], "relations": {}, "dateAdded": "2015-04-28T03:43:09Z", "dateModified": "2015-04-28T03:43:09Z" } }, { "key": "R55I969T", "version": 5, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/R55I969T", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/R55I969T", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/346853/items/62Z76RUC", "type": "application/json" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } } }, "data": { "key": "R55I969T", "version": 5, "parentItem": "62Z76RUC", "itemType": "attachment", "linkMode": "linked_url", "title": "Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling - Lozier - 2009 - Journal of Biogeography - Wiley Online Library", "accessDate": "2013-04-26T15:51:13Z", "url": "http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2699.2009.02152.x/pdf", "note": "", "contentType": "text/html", "charset": "utf-8", "tags": [], "relations": {}, "dateAdded": "2015-04-28T03:43:09Z", "dateModified": "2015-04-28T03:43:09Z" } }, { "key": "AFUWP4G5", "version": 20, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/AFUWP4G5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/AFUWP4G5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Araújo and Guisan", "parsedDate": "2006", "numChildren": 0 }, "data": { "key": "AFUWP4G5", "version": 20, "itemType": "journalArticle", "title": "Five (or so) challenges for species distribution modelling", "creators": [ { "creatorType": "author", "firstName": "Miguel B.", "lastName": "Araújo" }, { "creatorType": "author", "firstName": "Antoine", "lastName": "Guisan" } ], "abstractNote": "Species distribution modelling is central to both fundamental and applied research in biogeography. Despite widespread use of models, there are still important conceptual ambiguities as well as biotic and algorithmic uncertainties that need to be investigated in order to increase confidence in model results. We identify and discuss five areas of enquiry that are of high importance for species distribution modelling: (1) clarification of the niche concept; (2) improved designs for sampling data for building models; (3) improved parameterization; (4) improved model selection and predictor contribution; and (5) improved model evaluation. The challenges discussed in this essay do not preclude the need for developments of other areas of research in this field. However, they are critical for allowing the science of species distribution modelling to move forward.", "publicationTitle": "Journal of Biogeography", "publisher": "", "place": "", "date": "2006", "volume": "33", "issue": "10", "section": "", "partNumber": "", "partTitle": "", "pages": "1677-1688", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.1365-2699.2006.01584.x", "citationKey": "", "url": "http://dx.doi.org/10.1111/j.1365-2699.2006.01584.x", "accessDate": "2009-10-21T22:28:39Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Wiley InterScience", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "method" } ], "collections": [], "relations": {}, "dateAdded": "2015-04-28T03:41:57Z", "dateModified": "2015-04-28T03:41:57Z" } }, { "key": "W43P2BEE", "version": 28, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/W43P2BEE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/W43P2BEE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Elith and Leathwick", "parsedDate": "2007-05-01", "numChildren": 0 }, "data": { "key": "W43P2BEE", "version": 28, "itemType": "journalArticle", "title": "Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines", "creators": [ { "creatorType": "author", "firstName": "Jane", "lastName": "Elith" }, { "creatorType": "author", "firstName": "John", "lastName": "Leathwick" } ], "abstractNote": "Current circumstances — that the majority of species distribution records exist as presence-only data (e.g. from museums and herbaria), and that there is an established need for predictions of species distributions — mean that scientists and conservation managers seek to develop robust methods for using these data. Such methods must, in particular, accommodate the difficulties caused by lack of reliable information about sites where species are absent. Here we test two approaches for overcoming these difficulties, analysing a range of data sets using the technique of multivariate adaptive regression splines (MARS). MARS is closely related to regression techniques such as generalized additive models (GAMs) that are commonly and successfully used in modelling species distributions, but has particular advantages in its analytical speed and the ease of transfer of analysis results to other computational environments such as a Geographic Information System. MARS also has the advantage that it can model multiple responses, meaning that it can combine information from a set of species to determine the dominant environmental drivers of variation in species composition. We use data from 226 species from six regions of the world, and demonstrate the use of MARS for distribution modelling using presence-only data. We test whether (1) the type of data used to represent absence or background and (2) the signal from multiple species affect predictive performance, by evaluating predictions at completely independent sites where genuine presence–absence data were recorded. Models developed with absences inferred from the total set of presence-only sites for a biological group, and using simultaneous analysis of multiple species to inform the choice of predictor variables, performed better than models in which species were analysed singly, or in which pseudo-absences were drawn randomly from the study area. The methods are fast, relatively simple to understand, and useful for situations where data are limited. A tutorial is included.", "publicationTitle": "Diversity and Distributions", "publisher": "", "place": "", "date": "May 1, 2007", "volume": "13", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "265-275", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.1472-4642.2007.00340.x", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/j.1472-4642.2007.00340.x/abstract", "accessDate": "2014-08-04T03:37:54Z", "PMID": "", "PMCID": "", "ISSN": "1472-4642", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd", "extra": "", "tags": [ { "tag": "MARS", "type": 1 }, { "tag": "Pseudo-absence", "type": 1 }, { "tag": "community", "type": 1 }, { "tag": "habitat model", "type": 1 }, { "tag": "method" }, { "tag": "multiresponse", "type": 1 }, { "tag": "presence-only", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2015-04-28T03:39:19Z", "dateModified": "2015-04-28T03:39:19Z" } }, { "key": "C58B7H9U", "version": 23, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/C58B7H9U", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/C58B7H9U", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Warren et al.", "parsedDate": "2014", "numChildren": 0 }, "data": { "key": "C58B7H9U", "version": 23, "itemType": "journalArticle", "title": "Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern", "creators": [ { "creatorType": "author", "firstName": "Dan L.", "lastName": "Warren" }, { "creatorType": "author", "firstName": "Amber N.", "lastName": "Wright" }, { "creatorType": "author", "firstName": "Stephanie N.", "lastName": "Seifert" }, { "creatorType": "author", "firstName": "H. Bradley", "lastName": "Shaffer" } ], "abstractNote": "Aim\n\nEcological niche models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of model complexity and spatial sampling bias on niche models for 90 vertebrate taxa of conservation concern.\n\n\nLocation\n\nCalifornia, USA.\n\n\nMethods\n\nWe used Akaike information criterion (AICc) to select variables and tune Maxent's built-in regularization parameter (β) to constrain model complexity. In addition, we incorporated several estimates of spatial sampling bias based on interpolations of target group data. Ensemble forecasts were developed for future conditions from two emission scenarios and three climate change models for the year 2050.\n\n\nResults\n\nReducing the number of predictors and tuning β resulted in a reduction in the number of parameters in models built with sample sizes greater than approximately 10 occurrence points. Reducing the number of predictors had a substantially higher impact on the relative prioritization of different grid cells than did increasing regularization. There was little difference in prioritization of habitat when comparing models built using different spatial sampling bias estimates. Over half of the taxa were predicted to experience >80% reductions in environmental suitability in currently occupied cells, and this pattern was consistent across taxonomic groups.\n\n\nMain Conclusions\n\nOur results demonstrate that reducing the number of correlated predictor variables tends to decrease the breadth of models, while tuning regularization using AICc tends to increase it. These two strategies may provide a reasonable bracketing strategy for assessing climate change impacts.", "publicationTitle": "Diversity and Distributions", "publisher": "", "place": "", "date": "2014", "volume": "20", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "334-343", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Diversity Distrib.", "DOI": "10.1111/ddi.12160", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/ddi.12160/abstract", "accessDate": "2015-01-28T05:27:59Z", "PMID": "", "PMCID": "", "ISSN": "1472-4642", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2013 John Wiley & Sons Ltd", "extra": "", "tags": [ { "tag": "Climate Change", "type": 1 }, { "tag": "Maximum entropy", "type": 1 }, { "tag": "conservation", "type": 1 }, { "tag": "method" }, { "tag": "model complexity", "type": 1 }, { "tag": "niche modelling", "type": 1 }, { "tag": "sampling bias", "type": 1 }, { "tag": "species distribution modelling", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2015-04-28T03:39:19Z", "dateModified": "2015-04-28T03:39:19Z" } }, { "key": "3J67U3QF", "version": 22, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/3J67U3QF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/3J67U3QF", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Merow et al.", "parsedDate": "2014-12-01", "numChildren": 0 }, "data": { "key": "3J67U3QF", "version": 22, "itemType": "journalArticle", "title": "What do we gain from simplicity versus complexity in species distribution models?", "creators": [ { "creatorType": "author", "firstName": "Cory", "lastName": "Merow" }, { "creatorType": "author", "firstName": "Mathew J.", "lastName": "Smith" }, { "creatorType": "author", "firstName": "Thomas C.", "lastName": "Edwards" }, { "creatorType": "author", "firstName": "Antoine", "lastName": "Guisan" }, { "creatorType": "author", "firstName": "Sean M.", "lastName": "McMahon" }, { "creatorType": "author", "firstName": "Signe", "lastName": "Normand" }, { "creatorType": "author", "firstName": "Wilfried", "lastName": "Thuiller" }, { "creatorType": "author", "firstName": "Rafael O.", "lastName": "Wüest" }, { "creatorType": "author", "firstName": "Niklaus E.", "lastName": "Zimmermann" }, { "creatorType": "author", "firstName": "Jane", "lastName": "Elith" } ], "abstractNote": "Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence–environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence–environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building ‘under fit’ models, having insufficient flexibility to describe observed occurrence–environment relationships, we risk misunderstanding the factors shaping species distributions. By building ‘over fit’ models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.", "publicationTitle": "Ecography", "publisher": "", "place": "", "date": "December 1, 2014", "volume": "37", "issue": "12", "section": "", "partNumber": "", "partTitle": "", "pages": "1267-1281", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Ecography", "DOI": "10.1111/ecog.00845", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1111/ecog.00845/abstract", "accessDate": "2015-04-27T03:55:36Z", "PMID": "", "PMCID": "", "ISSN": "1600-0587", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2014 The Authors", "extra": "", "tags": [ { "tag": "method" }, { "tag": "niche modelling" } ], "collections": [], "relations": {}, "dateAdded": "2015-04-28T03:39:19Z", "dateModified": "2015-04-28T03:39:19Z" } }, { "key": "4M5R2D8J", "version": 22, "library": { "type": "group", "id": 346853, "name": "Species-distribution-modeling", "links": { "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/346853/items/4M5R2D8J", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/species-distribution-modeling/items/4M5R2D8J", "type": "text/html" } }, "meta": { "createdByUser": { "id": 115811, "username": "mkoo", "name": "Michelle Koo", "links": { "alternate": { "href": "https://www.zotero.org/mkoo", "type": "text/html" } } }, "creatorSummary": "Pearson and Dawson", "parsedDate": "2003", "numChildren": 0 }, "data": { "key": "4M5R2D8J", "version": 22, "itemType": "journalArticle", "title": "Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?", "creators": [ { "creatorType": "author", "firstName": "Richard G.", "lastName": "Pearson" }, { "creatorType": "author", "firstName": "Terence P.", "lastName": "Dawson" } ], "abstractNote": "Modelling strategies for predicting the potential impacts of climate change on the natural distribution of species have often focused on the characterization of a species' bioclimate envelope. A number of recent critiques have questioned the validity of this approach by pointing to the many factors other than climate that play an important part in determining species distributions and the dynamics of distribution changes. Such factors include biotic interactions, evolutionary change and dispersal ability. This paper reviews and evaluates criticisms of bioclimate envelope models and discusses the implications of these criticisms for the different modelling strategies employed. It is proposed that, although the complexity of the natural system presents fundamental limits to predictive modelling, the bioclimate envelope approach can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity. However, it is stressed that the spatial scale at which these models are applied is of fundamental importance, and that model results should not be interpreted without due consideration of the limitations involved. A hierarchical modelling framework is proposed through which some of these limitations can be addressed within a broader, scale-dependent context.", "publicationTitle": "Global Ecology & Biogeography", "publisher": "", "place": "", "date": "2003", "volume": "12", "issue": "5", "section": "", "partNumber": "", "partTitle": "", "pages": "361-371", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1046/j.1466-822X.2003.00042.x", "citationKey": "", "url": "http://dx.doi.org/10.1046/j.1466-822X.2003.00042.x", "accessDate": "2010-05-03T22:27:12Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Predicting the impacts of climate change on the distribution of species", "language": "", "libraryCatalog": "Wiley InterScience", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "method" }, { "tag": "niche modelling" } ], "collections": [], "relations": {}, "dateAdded": "2015-04-28T03:39:19Z", "dateModified": "2015-04-28T03:39:19Z" } } ]