Effects of physical environmental conditions on the species distribution and composition of marine fish and invertebrates along the Texas coast

了解环境条件的变化如何影响海洋生物区系，对于维护生物多样性和可持续渔业以及预测对未来气候情景的潜在反应至关重要。该项目的目的是确定鱼类和无脊椎动物的分布如何随着时间的推移沿着德克萨斯州海岸变化，并评估这些变化在多大程度上归因于当地环境条件的变化，如海面温度，沿海海平面，盐度，浊度和河流排放率。与大西洋和太平洋的沿海研究相比，墨西哥湾的生物系统研究还很缺乏。解决这些区域知识差距至关重要，因为墨西哥湾支持广泛多样的温带和热带物种，这些物种在生态和经济上具有重要意义。在沿着大西洋和太平洋沿岸，已观察到物种分布随着海面温度上升而向两极移动。相比之下，墨西哥湾的北方边缘是由土地，地方对沿海生物的潜在反应不断变化的环境条件的地理限制。该项目将使用先进的统计方法分析墨西哥湾西北部的长期物种组成数据，并描述过去物种组成与当地环境条件之间的关系。这些发现将有助于指导预测模型的开发，以评估对预测环境条件的潜在生物反应。研究结果将与负责管理沿海渔业的地方和国家资源机构分享。作为该项目的一个组成部分，将建立一个三级（学院-研究生-本科生）指导系统，通过本科生和研究生培训促进科学的多样性。本科生将通过科学促进奇卡诺人和美洲土著人协会（SACNAS）的德克萨斯A M大学分会招募，主要研究者目前是该协会的教员顾问。研究生和本科生都将作为一个团队参与该项目，并开发定量数据分析和其他一般科学技能。最后，该研究计划将被用作建立促进科学多样性的指导系统的案例研究。 长期物种组成数据的可用性提供了一个独特的机会，大大提高知识，了解气候变化对低纬度系统海洋生物的影响。该项目将研究分布在德克萨斯州海岸约650公里的8个海湾的物种组成数据;这种类型的综合数据在其他地方是罕见的。作为长期监测计划的一部分，这些生物数据已经收集了35-40年，包括1000多种鱼类和无脊椎动物的信息。这个独特的数据集将使用现代统计方法进行分析，包括占用数据分析，协整方法和状态空间向量自回归模型。这些方法克服了统计分析中常见的困难，包括自变量之间存在多重共线性的数据集和非平稳性数据集。根据统计分析的结果，将建立能够预测在预测的当地环境条件下的物种组成的模型。作为该项目的一部分，本科生和研究生将获得当代分析方法的专业知识，研究成果将与学术界和资源管理界广泛分享，并将提供计算代码。该项目将使人们更好地了解环境条件对鱼类和无脊椎动物分布的影响，并将为在不断变化的环境条件下改进渔业管理和养护工作提供宝贵的资料。

Understanding how changes in environmental conditions affect biota in the oceans is critically important for maintaining biodiversity and sustainable fisheries and projecting potential responses to future climate scenarios. The aims of this project are to determine how the distribution of fish and invertebrates has changed over time along the Texas coast and to assess the extent to which these changes are attributable to changes in local environmental conditions, such as sea surface temperature, coastal sea level, salinity, turbidity, and river discharge rate. Studies of biological systems in the Gulf of Mexico are lacking compared to coastal research in the Atlantic and Pacific oceans. Addressing these regional knowledge gaps is crucial because the Gulf of Mexico supports a wide diversity of temperate and tropical species that are ecologically and economically important. Poleward shifts in species distributions associated with increasing sea surface temperature have been observed along the Atlantic and Pacific coasts. In contrast, the northern edge of the Gulf of Mexico is bound by land that places biogeographic constraints on the potential responses of coastal organisms to changing environmental conditions. This project will use advanced statistical methods to analyze long-term species composition data for the northwestern Gulf of Mexico and characterize past relationships of species composition and local environmental conditions. These findings will help guide the development of predictive models to assess potential biological responses to projected environmental conditions. Research results will be shared with local and state resource agencies responsible for managing coastal fisheries. As an integral part of this project, a three-level (faculty-graduate-undergraduate) mentoring system will be established to promote diversity in science through undergraduate and graduate training. Undergraduate students will be recruited through the Texas A&M University Chapter of the Society for Advancement of Chicano and Native Americans in Science (SACNAS), for which the principal investigator is currently a faculty advisor. Both graduate and undergraduate students will work as a team on the project and develop quantitative data analysis and other general scientific skills. Finally, the research program will be used as a case study for establishing mentoring systems for promoting diversity in science. The availability of long-term species composition data provides a unique opportunity to substantially improve knowledge toward understanding the effects of climate change on marine organisms in a low latitude system. This project will examine species composition data for eight bays distributed over approximately 650 km of the Texas coast; comprehensive data of this type are uncommon elsewhere. The biological data have been collected over 35-40 years as part of a long-term monitoring program and includes information on more than 1000 species of fish and invertebrates. This unique dataset will be analyzed using modern statistical approaches, including occupancy data analysis, co-integration method, and state-space vector autoregressive modeling. These methods overcome common difficulties in statistical analyses, including datasets having multi-collinearity among independent variables and those involving non-stationarity. Based on the results of the statistical analyses, models enabling the prediction of species composition under projected local environmental conditions will be developed. As part of this project, undergraduate and graduate students will acquire expertise in contemporary analytical methods, research findings will be broadly shared with both the academic and resource management communities, and computational code will be made publically available. This project will provide better understanding of the effects of environmental conditions on fish and invertebrate distribution and will provide valuable information for improved fishery management and conservation efforts under changing environmental conditions.