Modelling removal and re-introduction data for improved conservation

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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.