The biodiversity data typically available for fitting distributional models in the tropics come from museum and scientific collections which are often incomplete and prone to sampling and environmental biases. Nevertheless, most studies undertaken in tropical regions assume that collection data offers a satisfactory environmental coverage without any quantitative assessment. In this study, we investigate the effects of differences in environmental bias and coverage provided by distributional data when aggregated into different grid cell sizes, on the performance of species richness-environment models and predictions. We use an extensive data compilation, including national and regional collections, on the distribution of amphibians, reptiles and fishes in the hydrologic region of the Usumacinta River as a case study. General additive models and environmental variables are used to construct predictive models at 40, 20, 10 and 5 km grid resolutions, based on well-sampled cells. The best multivariate models included nonparametric interaction terms for the effects of precipitation and temperature and suggested an altitudinal shift in the relative importance of energy and water in determining the distribution of species richness. For fishes, geomorphology accounted for fine scale variation in species richness along the hydrologic network, indicated by peaks in species diversity at the junction of the major rivers where major accumulation of water and sediments occurs. For all taxonomic groups, we found that sampling biases deviated most from the mean bias at the extremes of gradients accounting for important environmental factors. The pattern of environmental bias changed with grid size, with the form and amount of change being case-specific. Biases affected distribution predictions when compared with unbiased datasets. Moreover, not all models resulted best at coarser resolution as it is commonly assumed. Our results demonstrate that bias in the available data must be evaluated before mapping biodiversity distributions, irrespective of the choice of scale.

通常可用于拟合热带地区分布模型的生物多样性数据来自博物馆和科学馆藏，这些馆藏经常是不完整的，容易产生抽样和环境偏差。但是，大多数在热带地区进行的研究都假设收集数据无需任何定量评估即可提供令人满意的环境覆盖率。在这项研究中，我们调查了环境偏差和分布数据的差异（当聚合到不同的网格单元大小中时）对物种丰富度-环境模型和预测性能的影响。作为案例研究，我们使用了广泛的数据汇编，包括关于国家和地区的两栖动物，爬行动物和鱼类在乌苏马辛塔河水文地区的分布的数据。基于充分采样的单元格，一般的加性模型和环境变量用于构建40、20、10和5 km网格分辨率的预测模型。最佳的多元模型包括降水和温度影响的非参数相互作用项，并建议在确定物种丰富度的分布中，能量和水的相对重要性发生了垂直变化。对于鱼类而言，地貌学解释了沿水文网络物种丰富度的小尺度变化，这主要是由主要河流交界处的物种多样性峰值所致，那里主要发生水和沉积物的积累。对于所有分类学类别，我们发现在考虑极端环境因素的极端情况下，采样偏差与平均偏差的偏差最大。环境偏差的模式随网格大小而变化，变化的形式和变化量取决于具体情况。与无偏数据集相比，偏差会影响分布预测。而且，并非所有模型都在通常假定的较粗分辨率下获得最佳效果。我们的结果表明，在绘制生物多样性分布图之前，无论规模选择如何，都必须先评估可用数据中的偏差。