权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Modelling removal and re-introduction data for improved conservation

对移除和重新引入数据进行建模以改善保护

基本信息

批准号：
EP/S020470/2
负责人：
Rachel Sara McCrea
金额：
$ 2.98万
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FS020470%2F2
关键词：
Modelling removal re introduction data

项目摘要

Conservation monitoring schemes are constrained by time and cost and as such study design needs to be optimised to make the most of these available resources. Removal studies are conducted to protect target species from sites planned for development and the aim of such sampling is to capture and remove the entire population. Typically the studies are designed in an ad-hoc way with some repeated surveys on a single day, and some with simply daily visits. Sampling of sites is often avoided when weather conditions are considered not favourable. Removed species are trans-located to other habitats considered suitable for the specific species. However, measures to determine whether such translocations, and related re-introduction programmes have been successful are currently lacking. Developing robust approaches for both removal and re-introduction programmes will allow resources to be allocated optimally to ensure monitoring can be carried out for a sufficient period of time, to minimise the risk to the species under study.This project will develop new statistical approaches to make the most of the information available from removal and re-introduction data. The types of data which can be collected on animal populations are wide-ranging - for example, simple population counts, presence/absence data, presence only data, batch-marked data, and capture-recapture data. The difficulty and survey intensity required to collect these data will also depend on the associated skill set of data collector as well as the resources available to the team or individual responsible for designing the scheme. As well as proposing optimal study design for removal count data, the project will also address how to optimise study design if multiple types of data are collected simultaneously on a population. Further, we will explore how populations could be monitored with multiple types of data collection to better determine how successfully the population has established itself following some form of intervention (such as trans-location of individuals or re-introducing a previously locally extinct species back into an area).When fitting models to data it is possible to consider different structures to the model, for example to account for time-variation within detectability of the species, and therefore a model selection procedure needs to be implemented to select the structure of the model that best represents the observed data. Current approaches require an understanding of the statistical procedures implemented within this model selection step, however the methodological developments proposed within this project are aimed at a user-base who may have no such knowledge. Therefore within the project we will investigate the development of an automated procedure which will both select a best model(s) out of the models considered for the data set and will also assess how well the model(s) fits the observed data. A best candidate model may in fact fit the observed data very poorly and therefore this check of model fit is crucial if the results of the model will be used to make management decisions as otherwise erroneous conclusions could be drawn.Software with a graphical-user-interface will be developed to make the statistical developments accessible to those with no programming experience. The software will be web-based which will overcome operating system compatibility issues and user-manuals and tutorials will be produced to help end-users to make the most of the software's capabilities.

保护监测计划受到时间和成本的限制，因此需要优化研究设计，以充分利用这些可用资源。进行迁移研究是为了保护目标物种不受计划开发的地点的影响，这种取样的目的是捕捉和迁移整个种群。通常情况下，这些研究是以一种特别的方式设计的，有些是在一天内重复调查，有些是简单的每日访问。当天气条件被认为不利时，通常会避免对地点进行采样。移走的物种被转移到其他被认为适合特定物种的栖息地。然而，目前还没有采取措施来确定这种迁移以及相关的重新引入方案是否成功。为清除和重新引入计划制定强有力的方法，将使资源得到最佳分配，以确保监测能够进行足够的时间，从而最大限度地减少对所研究物种的风险。可以收集的动物种群数据类型范围很广，例如，简单的种群计数、存在/不存在数据、仅存在数据、批次标记数据和捕获-再捕获数据。收集这些数据所需的难度和调查强度还取决于数据收集者的相关技能以及负责设计方案的团队或个人可用的资源。除了为移除计数数据提出最佳研究设计外，该项目还将解决如何优化研究设计，如果同时收集人口的多种类型的数据。此外，我们将探讨如何通过多种类型的数据收集来监测人口，以更好地确定人口在某种形式的干预后是否成功地建立了自己（例如个体的迁移或将先前本地灭绝的物种重新引入到一个区域）。当将模型拟合到数据时，可以考虑模型的不同结构，例如，考虑到在物种的可检测性内的时间变化，因此，需要执行模型选择过程以选择最好地表示所观察到的数据的模型结构。目前的方法要求了解在这一模式选择步骤中实施的统计程序，但本项目中提出的方法发展是针对可能没有这种知识的用户群。因此，在该项目中，我们将研究自动程序的开发，该程序将从考虑用于数据集的模型中选择最佳模型，并评估模型与观察数据的拟合程度。一个最佳的候选模型实际上可能与观察到的数据拟合得很差，因此，如果模型的结果将用于作出管理决策，则这种模型拟合的检查是至关重要的，否则可能得出错误的结论。该软件将以网络为基础，将克服操作系统兼容性问题，并将编制用户手册和教程，以帮助最终用户充分利用该软件的功能。