Maximum entropy modeling of species geographic distributions

环境生态位模型 最大熵原理 航程(航空) 物种分布 数学 统计 生态学 计算机科学 生物 生态位 复合材料 栖息地 材料科学
作者
Steven J. Phillips,Robert P. Anderson,Robert E. Schapire
出处
期刊:Ecological Modelling [Elsevier]
卷期号:190 (3-4): 231-259 被引量:14970
标识
DOI:10.1016/j.ecolmodel.2005.03.026
摘要

The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present form for many applications with presence-only datasets, and merits further research and development.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
LUCKY发布了新的文献求助10
刚刚
陳新儒应助疏曲采纳,获得30
2秒前
smalls川发布了新的文献求助10
2秒前
4秒前
唐浩完成签到,获得积分10
4秒前
5秒前
5秒前
5秒前
6秒前
炎炎夏无声完成签到 ,获得积分10
8秒前
Nekozzzz发布了新的文献求助10
9秒前
9秒前
10秒前
缓慢珠发布了新的文献求助10
10秒前
大个应助高高诗柳采纳,获得10
10秒前
炸酱面完成签到,获得积分10
11秒前
坦率小天鹅给坦率小天鹅的求助进行了留言
12秒前
lalala发布了新的文献求助10
12秒前
天真的邴发布了新的文献求助10
12秒前
炸酱面发布了新的文献求助10
14秒前
大模型应助缓慢珠采纳,获得10
15秒前
风趣灵珊完成签到,获得积分10
15秒前
小吴同学来啦完成签到,获得积分10
16秒前
17秒前
科研宝完成签到,获得积分10
17秒前
17秒前
赘婿应助cloud采纳,获得10
18秒前
smalls川完成签到,获得积分10
18秒前
19秒前
研友_LaNYNn发布了新的文献求助10
21秒前
Jasper应助天真的邴采纳,获得10
21秒前
bkagyin应助天真的邴采纳,获得10
21秒前
三三得九发布了新的文献求助10
22秒前
22秒前
23秒前
23秒前
煜琪完成签到,获得积分10
24秒前
24秒前
25秒前
闲看花季完成签到,获得积分10
25秒前
高分求助中
Evolution 10000
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
Diagnostic immunohistochemistry : theranostic and genomic applications 6th Edition 500
Chen Hansheng: China’s Last Romantic Revolutionary 500
China's Relations With Japan 1945-83: The Role of Liao Chengzhi 400
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3149155
求助须知:如何正确求助?哪些是违规求助? 2800230
关于积分的说明 7839164
捐赠科研通 2457781
什么是DOI,文献DOI怎么找? 1308112
科研通“疑难数据库(出版商)”最低求助积分说明 628408
版权声明 601706