Panda Rajendra Mohan, Dash Padmanava, Sarathi Roy Partha
Department of Integrative Biology University of South Florida Tampa Florida USA.
Department of Geosciences Mississippi State University Starkville Mississippi USA.
Ecol Evol. 2024 Sep 29;14(10):e70364. doi: 10.1002/ece3.70364. eCollection 2024 Oct.
Understanding complexities in biodiversity is one of the fundamental goals of ecology and its monitoring is significant for ecosystem sustainability, maintenance, and conservation. However, biodiversity monitoring needs improvement to handle complex datasets and their analyses. This study attempts to understand these ecological complexities quickly, efficiently, and easily. The aim is to provide an alternative to ecologists, researchers, instructors, and stakeholders for biodiversity monitoring with the flexibility to visualize and customize outputs without software knowledge. A novel web-based technique is applied to monitor the biodiversity of a complex mountain ecosystem using a national database. The species-environment relationships of different vegetation types across a mountain ecosystem's elevation gradient are investigated using open-source climatic, physiographic, and socioeconomic variables. The proposed interactive tool to monitor biodiversity and understand its complexities is designed to visualize the data structure, summary, correlations, and sampling effectiveness quickly and easily. Plant species richness patterns and life forms (herb, shrub, and tree) across elevational gradients are investigated. We highlight the preliminary investigation of the data structure and their spatial distribution and apply the multicollinearity test to select variables for modeling. The drop-down menu helps users browse different datasets and select those datasets for instant visualization. Preliminary investigations on interactions between variables and species richness of vegetation types along elevation gradient interactively displayed with options to select variables, plant richness, and an elevational range. Species-environment relationships are investigated using multiple modeling protocols, and results are interactively displayed with options to download in different file formats and colors at the click of a button. This visualization tool helps to understand ecosystem structure, species richness patterns and species-environment relationships easily and efficiently. The R-codes used in this tool are reproducible and can be implemented with multiple datasets to monitor ecosystems.
理解生物多样性的复杂性是生态学的基本目标之一，对其进行监测对于生态系统的可持续性、维护和保护具有重要意义。然而，生物多样性监测需要改进以处理复杂的数据集及其分析。本研究试图快速、高效且轻松地理解这些生态复杂性。目的是为生态学家、研究人员、教师和利益相关者提供一种生物多样性监测的替代方法，使其能够灵活地可视化和定制输出，而无需软件知识。一种新颖的基于网络的技术被应用于使用国家数据库监测复杂山地生态系统的生物多样性。利用开源的气候、地形和社会经济变量，研究了山地生态系统海拔梯度上不同植被类型的物种 - 环境关系。所提出的用于监测生物多样性并理解其复杂性的交互式工具旨在快速、轻松地可视化数据结构、摘要、相关性和采样有效性。研究了海拔梯度上植物物种丰富度模式和生活型（草本、灌木和乔木）。我们强调了对数据结构及其空间分布的初步调查，并应用多重共线性检验来选择用于建模的变量。下拉菜单帮助用户浏览不同的数据集并选择那些数据集进行即时可视化。对沿海拔梯度的变量与植被类型物种丰富度之间的相互作用进行了初步调查，并以交互式方式显示，可选择变量、植物丰富度和海拔范围。使用多种建模协议研究物种 - 环境关系，并通过点击按钮以不同文件格式和颜色下载的选项交互式显示结果。这种可视化工具有助于轻松、高效地理解生态系统结构、物种丰富度模式和物种 - 环境关系。本工具中使用的R代码是可重复的，并且可以与多个数据集一起实施以监测生态系统。
