On using integral projection models to generate demographically driven predictions of species' distributions: development and validation using sparse data
On using integral projection models to generate demographically driven predictions of species' distributions: development and validation using sparse data
复制标题
关于使用积分投影模型生成人口统计驱动的物种分布预测：使用稀疏数据进行开发和验证
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
J. Silander
中科院分区：
文献类型：
--
作者：
C. Merow;A. Latimer;A. Wilson;S. McMahon;A. Rebelo;J. Silander
了解物种的地理分布对于理解和预测种群动态、对环境变化的响应、生物多样性模式和保护规划至关重要。虽然许多暗示的相关发生模型已被用于这些目的，但进展在于理解产生范围动态模式的潜在种群生物学。在这里，我们展示了如何使用有限数量的人口统计数据，使用规模结构人口的积分投影模型来生成人口分布模型（ddm）。通过使用回归对生存、生长和繁殖力进行建模，积分投影模型可以在缺失的大小数据和环境条件之间进行插值，以补偿有限的数据。为了适应与有限数据和模型假设相关的不确定性，我们使用贝叶斯模型将不确定性从模型开发的各个阶段传播到预测。ddm有很多优势：1)ddm允许对空间发生模式的机制理解；2) DDMs可以预测当地种群动态的时空变化；3) ddm可以在改变的环境条件下促进外推，因为人们可以评估个体生命率的后果。为了说明这些特征，我们构建了南非开普区变形科多年生灌木的ddm。我们发现物种的种群增长率受到整个范围内成虫存活率和高降雨量地区个体生长的强烈限制。虽然这些模型预测，在2050年的预测气候下，该地区核心地区的人口增长率会更高，但它们也表明，由于更频繁的火灾和干燥气候的相互作用，该物种在干旱地区的边缘地区面临着威胁。结果（和不确定性）有助于沿着这些梯度对特定人口参数的额外抽样进行优先排序，以迭代地改进预测。在附录中，我们提供了功能齐全的R代码来执行所有分析。
Knowledge of species' geographic distributions is critical for understanding and forecasting population dynamics, responses to environmental change, biodiversity patterns, and conservation planning. While many suggestive correlative occurrence models have been used to these ends, progress lies in understanding the underlying population biology that generates patterns of range dynamics. Here, we show how to use a limited quantity of demographic data to produce demographic distribution models (DDMs) using integral projection models for size-structured populations. By modeling survival, growth, and fecundity using regression, integral projection models can interpolate across missing size data and environmental conditions to compensate for limited data. To accommodate the uncertainty associated with limited data and model assumptions, we use Bayesian models to propagate uncertainty through all stages of model development to predictions. DDMs have a number of strengths: 1) DDMs allow a mechanistic understanding of spatial occurrence patterns; 2) DDMs can predict spatial and temporal variation in local population dynamics; 3) DDMs can facilitate extrapolation under altered environmental conditions because one can evaluate the consequences for individual vital rates. To illustrate these features, we construct DDMs for an overstory perennial shrub in the Proteaceae family in the Cape Floristic Region of South Africa. We find that the species' population growth rate is limited most strongly by adult survival throughout the range and by individual growth in higher rainfall regions. While the models predict higher population growth rates in the core of the range under projected climates for 2050, they also suggest that the species faces a threat along arid range margins from the interaction of more frequent fire and drying climate. The results (and uncertainties) are helpful for prioritizing additional sampling of particular demographic parameters along these gradients to iteratively refine projections. In the appendices, we provide fully functional R code to perform all analyses.