{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T02:58:21Z","timestamp":1776135501460,"version":"3.50.1"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Health Geogr"],"published-print":{"date-parts":[[2009,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Ecological niche modeling is a method for estimation of species distributions based on certain ecological parameters. Thus far, empirical determination of significant differences between independently generated distribution maps for a single species (maps which are created through equivalent processes, but with different ecological input parameters), has been challenging.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We describe a method for comparing model outcomes, which allows a statistical evaluation of whether the strength of prediction and breadth of predicted areas is measurably different between projected distributions. To create ecological niche models for statistical comparison, we utilized GARP (Genetic Algorithm for Rule-Set Production) software to generate ecological niche models of human monkeypox in Africa. We created several models, keeping constant the case location input records for each model but varying the ecological input data. In order to assess the relative importance of each ecological parameter included in the development of the individual predicted distributions, we performed pixel-to-pixel comparisons between model outcomes and calculated the mean difference in pixel scores. We used a two sample Student's t-test, (assuming as null hypothesis that both maps were identical to each other regardless of which input parameters were used) to examine whether the mean difference in corresponding pixel scores from one map to another was greater than would be expected by chance alone. We also utilized weighted kappa statistics, frequency distributions, and percent difference to look at the disparities in pixel scores. Multiple independent statistical tests indicated precipitation as the single most important independent ecological parameter in the niche model for human monkeypox disease.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>In addition to improving our understanding of the natural factors influencing the distribution of human monkeypox disease, such pixel-to-pixel comparison tests afford users the ability to empirically distinguish the significance of each of the diverse environmental parameters included in the modeling process. This method will be particularly useful in situations where the outcomes (maps) appear similar upon visual inspection (as are generated with other modeling programs such as MAXENT), as it allows an investigator the capacity to explore subtle differences among ecological parameters and to demonstrate the individual importance of these factors within an overall model.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1476-072x-8-7","type":"journal-article","created":{"date-parts":[[2009,1,31]],"date-time":"2009-01-31T07:13:24Z","timestamp":1233386004000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A method for statistically comparing spatial distribution maps"],"prefix":"10.1186","volume":"8","author":[{"given":"Rebecca S","family":"Levine","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krista L","family":"Yorita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew C","family":"Walsh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary G","family":"Reynolds","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2009,1,30]]},"reference":[{"key":"268_CR1","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1650\/0010-5422(2001)103[0599:PSGDBO]2.0.CO;2","volume":"103","author":"AT Peterson","year":"2001","unstructured":"Peterson AT: Predicting species' geographic distributions based on ecological niche modeling. The Condor. 2001, 103: 599-605. 10.1650\/0010-5422(2001)103[0599:PSGDBO]2.0.CO;2.","journal-title":"The Condor"},{"key":"268_CR2","doi-asserted-by":"publisher","first-page":"e176","DOI":"10.1371\/journal.pone.0000176","volume":"2","author":"RS Levine","year":"2007","unstructured":"Levine RS, Peterson AT, Yorita KL, Carroll D, Damon IK, Reynolds MG: Ecological niche and geographic distribution of human monkeypox in Africa. PLoS ONE. 2007, 2: e176-10.1371\/journal.pone.0000176.","journal-title":"PLoS ONE"},{"key":"268_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1128\/9781555816971.ch5","volume-title":"Emerging Infections 4","author":"JG Breman","year":"2000","unstructured":"Breman JG: Monkeypox: an Emerging Infection for Humans?. Emerging Infections 4. Edited by: Scheld WM, Craig WA, Hughes JM. 2000, Washington D.C.: ASM Press, 45-67."},{"key":"268_CR4","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3201\/eid1001.030125","volume":"10","author":"AT Peterson","year":"2004","unstructured":"Peterson AT, Bauer JT, Mills JN: Ecologic and geographic distribution of filovirus disease. Emerg Infect Dis. 2004, 10: 40-47.","journal-title":"Emerg Infect Dis"},{"key":"268_CR5","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1080\/136588199241391","volume":"13","author":"DRB Stockwell","year":"1999","unstructured":"Stockwell DRB, Peters D: The GARP modeling system: problems and solutions to automated spatial prediction. 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