Modelling removal and re-introduction data for improved conservation

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，以最大限度地利用这些可用资源。进行迁移研究是为了保护目标物种不受计划发展地点的影响，这种抽样的目的是捕获和迁移整个种群。通常，这些研究是以一种特别的方式设计的，其中一些是在一天内重复进行的调查，另一些是简单的日常访问。当天气条件被认为不利时，通常会避免对地点进行抽样。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，目前缺乏确定这种移位和相关的重新引入方案是否成功的措施。为清除和重新引入方案制定强有力的方法将使资源得到最佳分配，以确保可以在足够的时间内进行监测，将对被研究物种的风险降至最低。该项目将开发新的统计方法，以最大限度地利用从清除和重新引入数据中获得的信息。可以收集的关于动物种群的数据类型很广泛--例如，简单种群计数、存在/不存在数据、仅存在数据、批次标记数据和捕获-重新捕获数据。收集这些数据所需的难度和调查强度也将取决于数据收集人员的相关技能以及负责设计方案的团队或个人可用的资源。除了为清除计数数据提出最优研究设计外，该项目还将解决如何在人口中同时收集多种类型的数据时优化研究设计。此外，我们将探索如何通过多种类型的数据收集来监测种群，以更好地确定种群在某种形式的干预(如个体易位或将先前在当地灭绝的物种重新引入一个地区)后如何成功地建立自己。当将模型与数据进行拟合时，可以考虑模型的不同结构，例如考虑到物种可检测性内的时间变化，因此需要实施模型选择程序来选择最能代表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执行的统计程序，但这一项目中提议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，我们将研究自动化程序的开发，该程序将从为数据集考虑的模型中选择最佳模型(S)，并评估模型(S)与观测数据的匹配程度。一个最好的候选模型实际上可能非常不符合观察到的数据，因此，如果模型的结果将被用于作出管理决策，那么对模型适合性的检查是至关重要的，否则可能得出错误的结论。将开发具有图形用户界面的软件，使没有编程经验的人能够访问统计发展。该软件将以网络为基础，将克服操作系统兼容性问题，并将编写用户手册和教程，以帮助最终用户最大限度地利用该软件的功能。

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.