{"id":{"repo_id":"cuny","oai_identifier":"oai:academicworks.cuny.edu:cc_etds_theses-1004"},"canonical_url":"https://search.dev.ndltd.org/etd/cuny/oai:academicworks.cuny.edu:cc_etds_theses-1004","repository":{"repo_id":"cuny","name":"City University of New York - City College","base_url":"https://academicworks.cuny.edu/do/oai/"},"display":{"title":"The effect of the study region on GIS models of species geographic distributions and estimates of niche evolution; preliminary tests with montane rodents (genus Nephelomys) in Venezuela","abstract":"Various niche-based techniques exist to model a species' potential geographic distribution in a Geographic Information Systems (GIS) framework. These models compare the environmental conditions of localities of a species' occurrence versus those of the overall study region. In addition to uses in areas such as macroecology and conservation biology, this approach has been applied recently to studies of niche evolution and historical biogeography. Definition of the study region is critical for all of these applications but has not been addressed previously. Here, I examine the effect of changes in the extent of the study region on potential distribution models of two rodents (genus Nephelomys) in northern Venezuela. Models were produced using Maxent (a computer modeling program that utilizes the maximum-entropy principle), occurrence records from the literature, and 19 bioclimatic variables. First, I modeled each species in a large study region that included the ranges of both species (Method 1; typically employed in most studies to date). Second, I modeled each species in a smaller study region surrounding its respective localities, and then applied the model to the larger region (Method 2). Because the study region of Method 1 is likely to include areas of bioclimatically suitable habitat that are unoccupied by the species due to dispersal limitations and/or biotic interactions, this approach is prone to overfitting to conditions found near the known localities. In contrast, Method 2 is predicted to avoid such problems. I assessed differences in predictions for each species due to changes in the extent of the study region by calculating several measures of geographic interpredictivity between the species (indirect measures of niche overlap). Method 2 reduced problems 3 characteristic of overfitting. In addition, it led to higher—and likely more realistic—estimates of interpredictivity between the species, which suggests higher niche conservatism. Models of species‘ potential geographic distributions should be made using a study region that excludes areas of suitable conditions from which the species is known or likely to be absent because of dispersal limitations and/or biotic interactions.","abstract_html":"Various niche-based techniques exist to model a species' potential geographic distribution in a Geographic Information Systems (GIS) framework. These models compare the environmental conditions of localities of a species' occurrence versus those of the overall study region. In addition to uses in areas such as macroecology and conservation biology, this approach has been applied recently to studies of niche evolution and historical biogeography. Definition of the study region is critical for all of these applications but has not been addressed previously. Here, I examine the effect of changes in the extent of the study region on potential distribution models of two rodents (genus Nephelomys) in northern Venezuela. Models were produced using Maxent (a computer modeling program that utilizes the maximum-entropy principle), occurrence records from the literature, and 19 bioclimatic variables. First, I modeled each species in a large study region that included the ranges of both species (Method 1; typically employed in most studies to date). Second, I modeled each species in a smaller study region surrounding its respective localities, and then applied the model to the larger region (Method 2). Because the study region of Method 1 is likely to include areas of bioclimatically suitable habitat that are unoccupied by the species due to dispersal limitations and/or biotic interactions, this approach is prone to overfitting to conditions found near the known localities. In contrast, Method 2 is predicted to avoid such problems. I assessed differences in predictions for each species due to changes in the extent of the study region by calculating several measures of geographic interpredictivity between the species (indirect measures of niche overlap). Method 2 reduced problems 3 characteristic of overfitting. In addition, it led to higher—and likely more realistic—estimates of interpredictivity between the species, which suggests higher niche conservatism. Models of species‘ potential geographic distributions should be made using a study region that excludes areas of suitable conditions from which the species is known or likely to be absent because of dispersal limitations and/or biotic interactions.","abstract_has_math":false,"creators":["Raza, Ali"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis","degree_discipline":"Biology","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-01T08:00:00Z","date_published":"2010-01-01T08:00:00Z","updated_at":"2026-07-24T01:56:34Z","subjects":["Niche","Background sampling","Maxent","Presence-only modeling","Geographic Information Systems","Geographical distribution","Biogeography","Biology","Life Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/cc_etds_theses/5","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Raza, Ali"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Biology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Niche","Background sampling","Maxent","Presence-only modeling","Geographic Information Systems","Geographical distribution","Biogeography","Biology","Life Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/cc_etds_theses/5"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Various niche-based techniques exist to model a species' potential geographic distribution in a Geographic Information Systems (GIS) framework. These models compare the environmental conditions of localities of a species' occurrence versus those of the overall study region. In addition to uses in areas such as macroecology and conservation biology, this approach has been applied recently to studies of niche evolution and historical biogeography. Definition of the study region is critical for all of these applications but has not been addressed previously. Here, I examine the effect of changes in the extent of the study region on potential distribution models of two rodents (genus Nephelomys) in northern Venezuela. Models were produced using Maxent (a computer modeling program that utilizes the maximum-entropy principle), occurrence records from the literature, and 19 bioclimatic variables. First, I modeled each species in a large study region that included the ranges of both species (Method 1; typically employed in most studies to date). Second, I modeled each species in a smaller study region surrounding its respective localities, and then applied the model to the larger region (Method 2). Because the study region of Method 1 is likely to include areas of bioclimatically suitable habitat that are unoccupied by the species due to dispersal limitations and/or biotic interactions, this approach is prone to overfitting to conditions found near the known localities. In contrast, Method 2 is predicted to avoid such problems. I assessed differences in predictions for each species due to changes in the extent of the study region by calculating several measures of geographic interpredictivity between the species (indirect measures of niche overlap). Method 2 reduced problems 3 characteristic of overfitting. In addition, it led to higher—and likely more realistic—estimates of interpredictivity between the species, which suggests higher niche conservatism. Models of species‘ potential geographic distributions should be made using a study region that excludes areas of suitable conditions from which the species is known or likely to be absent because of dispersal limitations and/or biotic interactions."]},{"key":"dc:title","label":"Title","values":["The effect of the study region on GIS models of species geographic distributions and estimates of niche evolution; preliminary tests with montane rodents (genus Nephelomys) in Venezuela"]}]}],"canonical_facts":{"dc:creator":["Raza, Ali"],"dc:description.abstract":["Various niche-based techniques exist to model a species' potential geographic distribution in a Geographic Information Systems (GIS) framework. These models compare the environmental conditions of localities of a species' occurrence versus those of the overall study region. In addition to uses in areas such as macroecology and conservation biology, this approach has been applied recently to studies of niche evolution and historical biogeography. Definition of the study region is critical for all of these applications but has not been addressed previously. Here, I examine the effect of changes in the extent of the study region on potential distribution models of two rodents (genus Nephelomys) in northern Venezuela. Models were produced using Maxent (a computer modeling program that utilizes the maximum-entropy principle), occurrence records from the literature, and 19 bioclimatic variables. First, I modeled each species in a large study region that included the ranges of both species (Method 1; typically employed in most studies to date). Second, I modeled each species in a smaller study region surrounding its respective localities, and then applied the model to the larger region (Method 2). Because the study region of Method 1 is likely to include areas of bioclimatically suitable habitat that are unoccupied by the species due to dispersal limitations and/or biotic interactions, this approach is prone to overfitting to conditions found near the known localities. In contrast, Method 2 is predicted to avoid such problems. I assessed differences in predictions for each species due to changes in the extent of the study region by calculating several measures of geographic interpredictivity between the species (indirect measures of niche overlap). Method 2 reduced problems 3 characteristic of overfitting. In addition, it led to higher—and likely more realistic—estimates of interpredictivity between the species, which suggests higher niche conservatism. Models of species‘ potential geographic distributions should be made using a study region that excludes areas of suitable conditions from which the species is known or likely to be absent because of dispersal limitations and/or biotic interactions."],"dc:identifier":["https://academicworks.cuny.edu/cc_etds_theses/5"],"dc:subject":["Niche","Background sampling","Maxent","Presence-only modeling","Geographic Information Systems","Geographical distribution","Biogeography","Biology","Life Sciences"],"dc:title":["The effect of the study region on GIS models of species geographic distributions and estimates of niche evolution; preliminary tests with montane rodents (genus Nephelomys) in Venezuela"],"thesis:degree_discipline":["Biology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T01:56:34Z"}