SBIR Phase I: Assessing Status and Trends of Threatened Species from Uncertain Monitoring Data: Methodology and Software

小型企业创新研究(SBIR)第一阶段项目旨在开发和实施用于输入、处理和分析监测数据的软件方法，监测数据是来自调查、人口普查和其他常规评估的最基本形式的生物信息之一。这些方法将使用基本监测数据来(1)在种群一级评估被监测物种的状况和趋势，(2)估计更先进的量化模型的输入参数，从而增加对这些模型的使用，这些模型包括种群生存能力分析模型、生境模型和其他基于地理信息系统的方法，以及定量风险标准，如世界自然保护联盟(IUCN)和自然保护协会使用的标准。该软件的主要创新之一将是它对不确定性的处理。生态数据往往是稀缺和不确定的，包括时空变化、测量和抽样误差以及人口差异。将在软件中实施的方法将考虑到这种不确定性，并将其纳入现状评估和产生的其他产出。该项目的广泛影响将包括对广泛物种的监测程序实现标准化，大大降低处理和分析监测数据的成本，以及更多地使用与环境问题有关的先进定量模型和评估方法。这反过来将增加科学信息在环境决策和政策制定中的使用。该项目开发的方法还将允许以客观、透明和可信的方式纳入数据不确定性，从而为受监测物种的状况和趋势提供科学可信和合理的摘要。

This Small Business Innovation Research (SBIR) Phase I project aims to develop and implement as software methods for entering, processing, and analyzing monitoring data, which is one of the most basic forms of biological information that comes from surveys, censuses, and other routine assessments. These methods will use basic monitoring data to (1) assess the status and trends of the monitored species at the population-level, and (2) estimate the input parameters for the more advanced quantitative models, thereby increasing the use of these models, which include population viability analysis models, habitat models and other GIS-based methods, and quantitative risk criteria, such those used by the World Conservation Union (IUCN) and the NatureServe. One of the major innovations of the software will be its treatment of uncertainty. Ecological data are often scarce and uncertain, including spatial and temporal variation, measurement and sampling errors, and demographic variance. The methods to be implemented in the software will account for this uncertainty and incorporate it into the assessment of status and other outputs produced.Broader impacts of the project will include standardization of the monitoring process for a broad spectrum of species, significantly reduced the cost of processing and analyzing monitoring data, and increased use of advanced quantitative models and assessment methods in relation to environmental issues. This will, in turn, increase the use of scientific information in environmental decision-making and policy formulation. The methods developed in this project will also allow incorporating data uncertainties in an objective, transparent, and credible way, thereby providing scientifically credible and sound summary of the status and trends of the species monitored.