气候变化领域集成服务门户(CCPortal): Evaluating the skill of correlative species distribution models trained with mechanistic model output

CCPortal

DOI	10.1016/j.ecolmodel.2024.110692
	Evaluating the skill of correlative species distribution models trained with mechanistic model output
发表日期	2024
ISSN	0304-3800
EISSN	1872-7026
起始页码	491
卷号	491
英文摘要	Predicting the change in the distribution pattern of organisms is critical for assessing and mitigating risks associated with climate change and environmental variability. Correlative species distribution models (SDMs), which relate species' abundances to environmental data, are particularly useful for generating such predictions as they do not require a priori insight into the complex species' dynamics. Although correlative SDMs are typically developed using in situ environmental observations, their predictions are commonly created by applying SDMs with environmental information generated by mechanistic models. This can expand the temporal and spatial domain of the projections; however, this may also decrease the SDM prediction skill because of biases associated with the mechanistic model output. We test the hypothesis that training SDMs using environmental mechanistic model output may enhance model prediction skill by compensating for biases in the mechanistic model. We train SDMs for seven estuarine algal taxa observed in the Chesapeake Bay (U.S.A.) using both multi-decadal in situ environmental observations and mechanistic environmental output provided by a 3D coupled hydrodynamic-biogeochemical model. Training the SDMs using mechanistic model output, rather than in situ data, improves the model prediction skill by more than 10%. This demonstrates that although errors in SDM predictions can be caused by using imperfect environmental fields derived from mechanistic models, these errors may be diminished by training SDMs using these same environmental fields.
英文关键词	Species distribution modeling; Model prediction skill; Forecasting; Statistical training; Mechanistic models; Data assimilation; Generalized linear models; Harmful algal bloom; Chesapeake Bay
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Ecology
WOS记录号	WOS:001226917300001
来源期刊	ECOLOGICAL MODELLING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/288705
作者单位	William & Mary; Virginia Institute of Marine Science; University System of Maryland; University of Maryland Center for Environmental Science; University System of Maryland; University of Maryland College Park; National Aeronautics & Space Administration (NASA)
推荐引用方式 GB/T 7714	. Evaluating the skill of correlative species distribution models trained with mechanistic model output[J],2024,491.
APA	(2024).Evaluating the skill of correlative species distribution models trained with mechanistic model output.ECOLOGICAL MODELLING,491.
MLA	"Evaluating the skill of correlative species distribution models trained with mechanistic model output".ECOLOGICAL MODELLING 491(2024).

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