Results of the marginal test performed by DISTLM-forward analysis.

NIAID Data Ecosystem2026-03-08 收录

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Significant proportions of explained variation (PROP) are given in bold.

创建时间：

2014-01-08

Multi-species Occupancy Models Literature Review Dataset

生态统计建模

物种分布分析

This is a dataset used for a literature review on multi-species occupancy models (MSOMs) as part of a manuscript titled "Multi-species Occupancy Models: Review, Roadmap, and Recommendations" submitted

NIAID Data Ecosystem110

Parameter estimates from a Generalized linear mixed-effects model to estimate the differences in bottom depth (m) between a randomly sampled set of bottom depths (see methods for sampling details) and bottom depths at locations of VMT-tagged seals that were or were not associated with other VMT-tag

海洋哺乳动物栖息地分析

生态统计建模

Individual seals were treated as a random effect and bottom depths encountered by each seal were modelled as an AR(1) process (autocorrelation parameter estimate, φ = 0.274). Among-seal SD in bottom d

NIAID Data Ecosystem50

Standard deviation of the random factor.

生态统计建模

随机效应分析

We show the standard deviation of the best model in each set (in all cases this was the model including either individual or breeding pair identities, see Tables 3 and 4), and that of the same model s

Figshare2015-12-02 更新30

Generalised linear mixed model fitting of the data.

零膨胀混合模型

生态统计建模

The model used is of the form “glmm<-glmmadmb (parameter ~ locality+(1|month), zero Inflation = T, data = data,family = "pdf")”. KP = Kampung Paradason, LL = Limbuak Laut, TD = Timbang Dayang. AIC = A

NIAID Data Ecosystem60

Summary of generalized linear models results.

生态统计建模

蝙蝠多样性评估

Bat diversity parameters (species richness, abundance, functional richness and functional abundance) were tested as dependent variables. In all cases, the fitted model was Y = μ + Forest cover + Seaso

Figshare2016-12-08 更新40

Results of the marginal test performed by DISTLM-forward analysis.

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