SG: Environmental variation and optimal plant life history strategies of perennial plants

一个物种如何以及何时生长和繁殖被称为它的生活史策略。植物生活史策略的表现取决于温度、降雨和土壤养分。这些环境条件在世界各地和每年都有所不同。在很长一段时间内，一个物种的生活史策略会适应一个地点的温度、降雨和其他环境条件的范围。适合在沙漠中生存的植物可能不适合在雨林中生存，反之亦然。当特定地点的环境条件发生变化时，物种的生活史策略可能不再适应新的条件，其种群增长率可能受到影响。一个植物物种也许能够进化出一种新的策略，在变化的环境中更好地工作，但适应的速度可能跟不上快速的环境变化。在这种情况下，当地人口将减少，增加物种灭绝的风险。社会需要预测哪些植物在变化的环境中面临最大的风险，以有效地将稀缺资源分配给物种保护。本研究使用数学模型来预测不同环境下植物物种的最佳生活史策略，并量化一个物种对环境变化的适应程度。如果不测量多年来各种环境条件下植物的生长和繁殖情况，就很难对快速环境变化对植物生活史的影响进行预测。这样的长期数据集极为罕见，但在这项研究中，研究人员将利用一组30年的关于苦根沙雀（Astragalus scaphoides）植物生长和繁殖的数据集，来测试他们的模型产生的预测。本研究将植物生态学与数学建模相结合，并将为这两个学科的研究生和本科生提供培训。研究人员将构建预测生活史策略的通用模型，以生命周期繁殖或人口增长率来最大化适应性。假设自然选择选择了适应性最大化的基因型，优化模型预测了各种环境条件下的最佳生活史策略。具体而言，模型将回答以下问题：(1)在不同的环境条件（养分有效性、温度、降水）下，什么样的资源配置策略（生长、储存、繁殖）和休眠频率能优化多年生植物的适合度？(2)快速变化的环境条件对健康的影响是什么？此外，研究人员将使用多年生植物a . scaphoides的长期数据集，通过以下问题来测试模型预测：(3)观察到的温度和降水变化的幅度是否足以对植物适应性产生负面影响？

How and when a species grows and reproduces is called its life history strategy. The performance of a plant's life history strategy depends on temperature, rainfall and soil nutrients. These environmental conditions vary around the world and from year to year. Over a long period of time, the life history strategy of a species adapts to the ranges of temperature, rainfall and other environmental conditions at a location. A plant adapted to do well in a desert may not be well adapted to live in a rain forest, and vice versa. When environmental conditions in a particular location change, a species' life history strategy may no longer be adapted to the new conditions, and its population growth rate may suffer. A plant species may be able to evolve a new strategy that works better in the changed environment, but the rate of adaptation may not keep pace with rapid environmental change. In that case local populations will shrink, increasing the risk of the species' extinction. Society needs to predict which plants are most at risk from changed environments to efficiently allocate scarce resources to species conservation. This study uses mathematical models to predict the best life history strategy for plant species living different environments, and to quantify how poorly adapted a species is to a change in the environment. Testing predictions of the effects of rapid environmental change on plant life history is difficult without measuring plant growth and reproduction in many years with a wide range of environmental conditions. Such long-term data sets are extremely rare, but in this study researchers will make use of a 30 year-long data set on the growth and reproduction of the plant species bitterroot milkvetch (Astragalus scaphoides), to test the predictions generated by their models. This research combines plant ecology and mathematical modeling, and will provide training for graduate and undergraduate students in both disciplines. The researchers will construct general models predicting life history strategies that maximize fitness measured as life time reproduction or population growth rate. Assuming natural selection selects for genotypes that maximize fitness, optimization models predict optimal life history strategies for various environmental conditions. Specifically, the models will answer the following questions: (1) What resource allocation strategy (growth, storage, reproduction) and dormancy frequency optimizes the fitness of perennial plants under different environmental conditions (nutrient availability, temperature, precipitation)? (2) What are the fitness consequences of rapidly changing environmental conditions? Further, the researchers will use a long term data set on performance of a perennial plant, A. scaphoides, to test the model predictions by asking: (3) Is the magnitude of observed variation in temperature and precipitation sufficient to negatively affect plant fitness?