Controlling the effects of sampling bias in biodiversity models

IF 3.4 2区 环境科学与生态学 Q2 ECOLOGY Journal of Biogeography Pub Date : 2024-04-17 DOI:10.1111/jbi.14851
Ubirajara Oliveira, Britaldo Soares-Filho, Felipe Nunes
{"title":"Controlling the effects of sampling bias in biodiversity models","authors":"Ubirajara Oliveira,&nbsp;Britaldo Soares-Filho,&nbsp;Felipe Nunes","doi":"10.1111/jbi.14851","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aim</h3>\n \n <p>Sampling bias and gaps have a direct influence on the perceived patterns of biodiversity, hence limiting our ability to make well-informed decisions about biodiversity conservation. Yet most methods either disregard or underestimate the effects of sampling bias and gaps in modelling biodiversity patterns. Our objective is to test the sensitivity of commonly used methods for modelling biodiversity dimensions (richness, endemism, and beta diversity) to sampling bias and collection gaps, and as a way to mitigate those effects we introduce a novel approach that employs the sampling effort to minimize the effects of collection bias and gaps in biodiversity models.</p>\n </section>\n \n <section>\n \n <h3> Location</h3>\n \n <p>South America.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Here, we use controlled simulations of virtual species distribution and sampling effort to test the sensitivity to sampling bias and collection gaps by commonly used methods, that is, species distribution models (SDMs), spatial interpolation (SI), and environmental prediction (EP), for estimating species richness, endemism, and beta diversity. Our research contributes to advancing biodiversity modelling by introducing a novel approach, named uniform sampling from sampling effort (USSE), that employs the sampling effort to minimize the effects of collection bias and gaps.</p>\n </section>\n \n <section>\n \n <h3> Results and Main Conclusions</h3>\n \n <p>EP with USSE has proven effective in accurately predicting species richness, especially in scenarios in which the sampling effort does not coincide with the biodiversity niches. It outperformed SI and SDMs. The latter performed poorly, yielding the lowest predictive score. In estimating endemism and beta diversity, all methods yielded similar results, without statistically significant differences. For estimating beta diversity, the generalized dissimilarity model proved to be a robust method, even in face of biased sampling. Controlled simulations are key to testing biodiversity methods. These tests can isolate confounding factors inherent to real-world data, enabling robust methodological assessments. Although fieldwork and curation of collections must remain indispensable, novel biodiversity methods could help overcome the limitations of sampling biases, helping expedite conservation actions much needed.</p>\n </section>\n </div>","PeriodicalId":15299,"journal":{"name":"Journal of Biogeography","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biogeography","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jbi.14851","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Aim

Sampling bias and gaps have a direct influence on the perceived patterns of biodiversity, hence limiting our ability to make well-informed decisions about biodiversity conservation. Yet most methods either disregard or underestimate the effects of sampling bias and gaps in modelling biodiversity patterns. Our objective is to test the sensitivity of commonly used methods for modelling biodiversity dimensions (richness, endemism, and beta diversity) to sampling bias and collection gaps, and as a way to mitigate those effects we introduce a novel approach that employs the sampling effort to minimize the effects of collection bias and gaps in biodiversity models.

Location

South America.

Methods

Here, we use controlled simulations of virtual species distribution and sampling effort to test the sensitivity to sampling bias and collection gaps by commonly used methods, that is, species distribution models (SDMs), spatial interpolation (SI), and environmental prediction (EP), for estimating species richness, endemism, and beta diversity. Our research contributes to advancing biodiversity modelling by introducing a novel approach, named uniform sampling from sampling effort (USSE), that employs the sampling effort to minimize the effects of collection bias and gaps.

Results and Main Conclusions

EP with USSE has proven effective in accurately predicting species richness, especially in scenarios in which the sampling effort does not coincide with the biodiversity niches. It outperformed SI and SDMs. The latter performed poorly, yielding the lowest predictive score. In estimating endemism and beta diversity, all methods yielded similar results, without statistically significant differences. For estimating beta diversity, the generalized dissimilarity model proved to be a robust method, even in face of biased sampling. Controlled simulations are key to testing biodiversity methods. These tests can isolate confounding factors inherent to real-world data, enabling robust methodological assessments. Although fieldwork and curation of collections must remain indispensable, novel biodiversity methods could help overcome the limitations of sampling biases, helping expedite conservation actions much needed.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
控制生物多样性模型中抽样偏差的影响
目的取样偏差和差距对生物多样性的感知模式有直接影响,从而限制了我们在生物多样性保护方面做出明智决策的能力。然而,在建立生物多样性模式模型时,大多数方法不是忽略就是低估了取样偏差和差距的影响。我们的目标是测试常用的生物多样性建模方法(丰富度、特有性和贝塔多样性)对取样偏差和收集差距的敏感性,作为减轻这些影响的一种方法,我们引入了一种新方法,利用取样工作将生物多样性模型中收集偏差和差距的影响降至最低。地点:南美洲方法:在此,我们利用虚拟物种分布和取样努力的受控模拟,测试常用方法(即物种分布模型(SDM)、空间插值(SI)和环境预测(EP))对取样偏差和收集差距的敏感性,以估算物种丰富度、特有性和β多样性。我们的研究引入了一种名为 "取样努力均匀取样"(USSE)的新方法,利用取样努力最大限度地减少采集偏差和差距的影响,从而为推动生物多样性建模做出了贡献。它的表现优于 SI 和 SDM。后者表现不佳,预测得分最低。在估计特有性和贝塔多样性方面,所有方法的结果相似,在统计上没有显著差异。在估计贝塔多样性方面,广义相似性模型被证明是一种稳健的方法,即使在取样存在偏差的情况下也是如此。受控模拟是测试生物多样性方法的关键。这些测试可以隔离现实世界数据中固有的干扰因素,从而进行可靠的方法评估。虽然实地考察和收集整理工作仍然不可或缺,但新的生物多样性方法有助于克服取样偏差的局限性,帮助加快亟需的保护行动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Biogeography
Journal of Biogeography 环境科学-生态学
CiteScore
7.70
自引率
5.10%
发文量
203
审稿时长
2.2 months
期刊介绍: Papers dealing with all aspects of spatial, ecological and historical biogeography are considered for publication in Journal of Biogeography. The mission of the journal is to contribute to the growth and societal relevance of the discipline of biogeography through its role in the dissemination of biogeographical research.
期刊最新文献
Issue Information Cover Species Distribution Models for Mesopelagic Mesozooplankton Community Issue Information Cover
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1