{"title":"结合社区科学和MaxEnt模型估算野生火鸡(Meleagris gallopavo)冬季丰度和分布","authors":"Jennifer E. Baici, J. Bowman","doi":"10.5751/ace-02390-180108","DOIUrl":null,"url":null,"abstract":". Understanding the distribution and abundance of species is a fundamental aspect of conservation biology. Species distribution models aim to predict distributions based on species observations and ecologically relevant information. To understand the contemporary distribution of Wild Turkeys ( Meleagris gallopavo ) in Ontario, we curated and collated Wild Turkey flock observations from eBird and iNaturalist submitted during winter 2018. We combined these with environmental predictors to build distribution models using MaxEnt and evaluated model fit using 10-fold cross validation. We also estimated total population size for this species under different modeling scenarios. The potential presence of unknown spatial bias in community science datasets is a complex problem often requiring context-specific statistical solutions. Data cleaning, sometimes referred to as thinning, filtering, or culling, is often proposed to manage this bias. As such, we tested the effect of data cleaning on model outputs and on subsequent analyses. We evaluated all models using area under the curve (AUC). We found building density to be the most important environmental variable followed by winter severity. We validated our habitat suitability estimates using fine-scale GPS data and found that data cleaning had no effect on habitat suitability estimates inside available Wild Turkey habitat or inside core-use areas, except at one site in 2012 (t = -2.2, P = 0.04, df = 14). Use of community collected data offers a cost-efficient and collaborative method to obtain data for species distribution modeling and management. We discuss implications for Wild Turkey management and present potential contemporary distribution maps for this species.","PeriodicalId":49233,"journal":{"name":"Avian Conservation and Ecology","volume":"1 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Combining community science and MaxEnt modeling to estimate Wild Turkey ( Meleagris gallopavo ) winter abundance and distribution\",\"authors\":\"Jennifer E. Baici, J. Bowman\",\"doi\":\"10.5751/ace-02390-180108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". Understanding the distribution and abundance of species is a fundamental aspect of conservation biology. Species distribution models aim to predict distributions based on species observations and ecologically relevant information. To understand the contemporary distribution of Wild Turkeys ( Meleagris gallopavo ) in Ontario, we curated and collated Wild Turkey flock observations from eBird and iNaturalist submitted during winter 2018. We combined these with environmental predictors to build distribution models using MaxEnt and evaluated model fit using 10-fold cross validation. We also estimated total population size for this species under different modeling scenarios. The potential presence of unknown spatial bias in community science datasets is a complex problem often requiring context-specific statistical solutions. Data cleaning, sometimes referred to as thinning, filtering, or culling, is often proposed to manage this bias. As such, we tested the effect of data cleaning on model outputs and on subsequent analyses. We evaluated all models using area under the curve (AUC). We found building density to be the most important environmental variable followed by winter severity. We validated our habitat suitability estimates using fine-scale GPS data and found that data cleaning had no effect on habitat suitability estimates inside available Wild Turkey habitat or inside core-use areas, except at one site in 2012 (t = -2.2, P = 0.04, df = 14). Use of community collected data offers a cost-efficient and collaborative method to obtain data for species distribution modeling and management. We discuss implications for Wild Turkey management and present potential contemporary distribution maps for this species.\",\"PeriodicalId\":49233,\"journal\":{\"name\":\"Avian Conservation and Ecology\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Avian Conservation and Ecology\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.5751/ace-02390-180108\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIODIVERSITY CONSERVATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Avian Conservation and Ecology","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.5751/ace-02390-180108","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIODIVERSITY CONSERVATION","Score":null,"Total":0}
Combining community science and MaxEnt modeling to estimate Wild Turkey ( Meleagris gallopavo ) winter abundance and distribution
. Understanding the distribution and abundance of species is a fundamental aspect of conservation biology. Species distribution models aim to predict distributions based on species observations and ecologically relevant information. To understand the contemporary distribution of Wild Turkeys ( Meleagris gallopavo ) in Ontario, we curated and collated Wild Turkey flock observations from eBird and iNaturalist submitted during winter 2018. We combined these with environmental predictors to build distribution models using MaxEnt and evaluated model fit using 10-fold cross validation. We also estimated total population size for this species under different modeling scenarios. The potential presence of unknown spatial bias in community science datasets is a complex problem often requiring context-specific statistical solutions. Data cleaning, sometimes referred to as thinning, filtering, or culling, is often proposed to manage this bias. As such, we tested the effect of data cleaning on model outputs and on subsequent analyses. We evaluated all models using area under the curve (AUC). We found building density to be the most important environmental variable followed by winter severity. We validated our habitat suitability estimates using fine-scale GPS data and found that data cleaning had no effect on habitat suitability estimates inside available Wild Turkey habitat or inside core-use areas, except at one site in 2012 (t = -2.2, P = 0.04, df = 14). Use of community collected data offers a cost-efficient and collaborative method to obtain data for species distribution modeling and management. We discuss implications for Wild Turkey management and present potential contemporary distribution maps for this species.
期刊介绍:
Avian Conservation and Ecology is an open-access, fully electronic scientific journal, sponsored by the Society of Canadian Ornithologists and Birds Canada. We publish papers that are scientifically rigorous and relevant to the bird conservation community in a cost-effective electronic approach that makes them freely available to scientists and the public in real-time. ACE is a fully indexed ISSN journal that welcomes contributions from scientists all over the world.
While the name of the journal implies a publication niche of conservation AND ecology, we think the theme of conservation THROUGH ecology provides a better sense of our purpose. As such, we are particularly interested in contributions that use a scientifically sound and rigorous approach to the achievement of avian conservation as revealed through insights into ecological principles and processes. Papers are expected to fall along a continuum of pure conservation and management at one end to more pure ecology at the other but our emphasis will be on those contributions with direct relevance to conservation objectives.