英语翻译“Quadrat” was modeled as a random effect to account for thegrouping of observations within the same quadrat.“Method” and “Reef” weremodeled as fixed effects.The minimal adequate model was derived by stepwisedeletion of terms a

英语翻译“Quadrat” was modeled as a random effect to account for thegrouping of observations within the same quadrat.“Method” and “Reef” weremodeled as fixed effects.The minimal adequate model was derived by stepwisedeletion of terms a
英语翻译
“Quadrat” was modeled as a random effect to account for the
grouping of observations within the same quadrat.“Method” and “Reef” were
modeled as fixed effects.The minimal adequate model was derived by stepwise
deletion of terms and model selection was based on AIC (lower values indicate a
“better” model fit to the data).To assess the significance of terms in the
final model,the depleted model was compared to the full model by calculating
the deviance and comparing it to the value obtained from a chi-squared
distribution.The p values presented here should
be treated with caution because they can be unreliable with small sample sizes
(Bolker et al.2008).Point and interval estimates of model parameters
are more informative and were extracted from the final model according to
Pinheiro and Bates (2000).Behavior of the variance and residuals
was assessed by examining plots of residuals vs fitted values and normal
quantile–quantile plots,respectively.Linear regression was used to model the
relationship between substrate complexity and the difference between methods.All analyses were performed in R 2.4.1 (R Development Core Team 2006)

英语翻译“Quadrat” was modeled as a random effect to account for thegrouping of observations within the same quadrat.“Method” and “Reef” weremodeled as fixed effects.The minimal adequate model was derived by stepwisedeletion of terms a
“Quadrat” was modeled as a random effect to account for the grouping of observations within the same quadrat.“Method” and “Reef” were modeled as fixed effects.The minimal adequate model was derived by stepwise deletion of terms and model selection was based on AIC (lower values indicate a “better” model fit to the data).To assess the significance of terms in the final model,the depleted model was compared to the full model by calculating the deviance and comparing it to the value obtained from a chi-squared distribution.The p values presented here should be treated with caution because they can be unreliable with small sample sizes (Bolker et al.2008).Point and interval estimates of model parameters are more informative and were extracted from the final model according to Pinheiro and Bates (2000).Behavior of the variance and residuals was assessed by examining plots of residuals vs fitted values and normal quantile–quantile plots,respectively.Linear regression was used to model the relationship between substrate complexity and the difference between methods.All analyses were performed in R 2.4.1 (R Development Core Team 2006)
“样方”建成随机效应模型,来解释同一样方内观测值的分组（情况）.“方法”、“暗礁”设为固定效应值.通过逐步删除条件项得到最小充分模型（MAM）,并根据AIC选择模型（AIC数值愈小,愈说明模型“更好”、更符合数据）.要评价最终模型各项统计学意义（或：显著性）,需通过计算变异（度）、将变异和χ2分布得到的数值进行比较、再来对比剔过的模型和原始未剔除模型.因样方样本量过小、（结果）不可靠,应谨慎看待这里所得P值（Bolker等,2008年）.而按Pinheiro、Bates的观点（2000年）,模型参数点估计值、区间估计包含更多信息,并可由最终模型求得.通过（观察）拟合值-残差图、分位数图分别评价方差、残差情况.线性回归用于构建基板复杂性关系及各方法间的差异.采用2.4.1版R软件进行上述分析.（R软件核心开发团队,2006年）
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“方”是仿照作为一个随机效应帐户的观测值分组在同一个样方。“方法”和“礁”模型固定效应。最小模型所得逐步删除条款及模型的选择是根据汽车工业（低值表明一个“更好”的模型拟合数据）。评估的意义，在最后的模型，枯竭模型是比较完整的模型计算的越轨行为和比较的价值得到了卡方分布。这里提出的价值应谨慎对待，因为他们是不可靠的小样本大小（bolker等人。2008。点和区间估计模型参数的更多信息和提取的最终模型...

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“方”是仿照作为一个随机效应帐户的观测值分组在同一个样方。“方法”和“礁”模型固定效应。最小模型所得逐步删除条款及模型的选择是根据汽车工业（低值表明一个“更好”的模型拟合数据）。评估的意义，在最后的模型，枯竭模型是比较完整的模型计算的越轨行为和比较的价值得到了卡方分布。这里提出的价值应谨慎对待，因为他们是不可靠的小样本大小（bolker等人。2008。点和区间估计模型参数的更多信息和提取的最终模型根据皮涅罗和贝茨（2000）。行为差异和残差评估审查小块残差和拟合值和正常的位数，位数地块，分别。线性回归是用来模型之间的关系基板的复杂性和方法之间的差异。所有的分析进行的4（核心开发团队2006）

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