Linear regression analyses for survival vs. fouling cover (Figures 5 a, b).
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NS = not significant.
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2015-12-02
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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
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.
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
Parameter estimates from a model (logistic regression) of Seaside Sparrow nest site characteristics (vs random points) in Louisiana, USA, 2012–2017.
Parameter estimates from a model (logistic regression) of Seaside Sparrow nest site characteristics (vs random points) in Louisiana, USA, 2012–2017.
Figshare2021-10-26 更新00
Summary of generalized linear models results.
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
Tested carcass density distribution models with corrected Akaike’s information criterion (AICc) and delta (Δ) AICc, and fitted parameters.
Tested carcass density distribution models with corrected Akaike’s information criterion (AICc) and delta (Δ) AICc, and fitted parameters.
Figshare2018-12-12 更新20
