Working in collaboration with University of Edinburgh on utilising open-access Earth Observation (EO) data and a combination of machine and deep learning pipelines e.g. random forests, support vector machines, logistic regression and convolutional neural networks (CNN) to classify habitats and generate landscape scale habitat maps that can be utilised in both ecological research and for the Environmental Impact Assessment process.

Working in collaboration with Bavarian Forest National Park and Šumava National Park to assess the effect of recolonising grey wolves (Canis Lupus) on red deer (Cervus elaphus) populations in the wider Bohemian Forest Ecosystem. Using hyper-realistic agent-based simulations parameterised with real world telemetry, monitoring and environmental GIS data this project will predict the inter-annual fluctuations in red deer populations for park management to set hunting quotas. 

Working in collaboration with Ireland's Department of Agriculture, Food and the Marine (DAFM) to assess the role of European badgers (Meles meles) in Bovine Tuberculosis (bTB) in Irelands agr0-ecological landscapes. Using hyper-realistic agent-based simulations parameterised with real world telemetry, monitoring and environmental GIS data this project will predict how disturbances and management regimes alter disease risk. 

Working for Teagasc as a Research Officer on the Agri_Birds project. The study will provide information to policy-makers on the effects and likely impact of Nitrates Action Programme (NAP)  measures on farmland birds. Results of this project will inform and facilitate policy-makers in decision-making in the design of future NAP recommendations, and the integration of NAP into wider agri-environment programmes. The primary aim is to minimise potential trade-offs in the delivery of environmental objectives.

Ph.D project supervised by Dr. Simone Ciuti, in collaboration with, and funded by, the Department of Agriculture, Food and the Marine (DAFM). This project examined multiple human-wildlife case studies in Ireland and utilised state of the art techniques to draw as much inference and predictive capabilities from low quality data. The project also led to an 8 week lecture tour and collaborative visits to over 10 research institutions in the United States of America and Canada to draw on their experiences in wildlife management and to inform them of the research being done in Ireland. 

The SMARTDEER project - funded by DAFM and UCD - is a research program led by UCD researchers aimed at producing up-to-date distribution maps of deer species in Ireland. This has been achieved by combining historical deer data collected across the nation (2000 - 2023) with new digital tools (web survey and smartphone application) able to guarantee a steady flow of deer data thanks to the help of all stakeholders and citizens involved in the research program. 

Project Website available here: 

SMARTDEER IRELAND

Working in collaboration with Coillte, we aimed to disentangle the relationship between forest damage, forest characteristics and the roles deer play in damaging forest ecosystems. To achieve this, we integrated novel high resolution deer distribution data for multiple deer species (native and non-native) with forest inventory data collected in 1,681 sampling stations across Ireland to (i) understand environmental drivers of damage, and (ii) demonstrate the utility of this approach in providing predicted damage scenarios based on the relative abundance of different co-occurring deer species. 

Published paper available here:

Brock et al. 2023

The impact of perturbation events, such as forest clearfelling, on disease dynamics is difficult to quantify and poorly understood, especially in multi-host systems. In collaboration with DAFM we investigated the dynamics of bovine tuberculosis (bTB) in Ireland. Supervising a research M.Sc student Renée Khouri, we investigated the effect of the interaction between clearfell and relative densities of badgers (Meles meles), red deer (Cervus elaphus), fallow deer (Dama dama), and sika deer (Cervus nippon) on the relative risk of bTB breakdown in cattle herds. We fit conditional logistic regression models to farm (n = 33054) data at four spatial and temporal scales to model space (2 or 5 km) and time (0–12 or 24–36 months) to investigate how bTB risk was modulated by forest clearfell.

Here, we used GIS-integrated ABMs to study the outcome of wolf reintroduction to Ireland’s national parks with respect to wolf ecology and wolf-livestock interactions. We introduced management strategies and policy interventions to assess how wolf-livestock interactions could be influenced by wildlife managers and whether outcomes were site-specific. 

Published paper available here: 

 Murphy et al. 2023

We undertook a scoping review of multi-host bTB epidemiology to identify recent trends in species publication focus, methodologies, scales and One Health approaches. We aimed to identify research gaps where novel research could provide insights to inform control policy, for bTB and other zoonoses. 

Published paper available here: 

Conteddu et al. 2023

 Here, our aim was to define Woodcock’s presence in Italy during the post-nuptial migration, the wintering phase, and at the beginning of the pre-nuptial migration phase, using monitoring data collected between September and March for the period 2016 to 2021. We modelled the abundance of Woodcock as a function of biotic (habitat type, vegetation) and abiotic (place, season, temperature, altitude) factors to assess the presence of Woodcock in Italy. 

Published paper available here:

Tuti et al. 2023

In collaboration with University of Alberta and North Dakota Game and Fish, we used a generalised additive models (GAMs) on a 50-year time series (1962–2012) database on mule deer (Odocoileus hemionus) demographics, seasonal weather, predator density, and oil and gas development patterns from the North Dakota Badlands, USA, to investigate long-term effects of landscape-level disturbance on mule deer fawn fall recruitment, which has declined precipitously over the last number of decades. 

Published paper available here:
Murphy et al. 2023

We propose a framework for zoonotic disease classification which combines four known classification types (pathogen type, life cycle, transmission direction and ecosystem) into one systematic method. We chose to focus on four specific aspects to provide as broad an overview as possible of zoonoses, in order to inform the risk of transmission to humans.This framework can be applied to the zoonoses occurring in any non-human animal species and supports the generation of systematically collected empirical data that are useful for monitoring the spatio-temporal dynamics of zoonoses.

Paper available here: 

Keenan et al. 2023

Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high-resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyze data for wildlife populations. Based on high-resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5 × 5 km or finer resolution), thereby converting low-resolution data into a high-resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika, and fallow) in Ireland from 2000 to 2018.  

Published Paper available here:

Murphy et al. 2023

Integrated species distribution models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted, PA data with the lower-quality, unstructured, but usually more extensive PO datasets. Joint-likelihood ISDMs can be run in a Bayesian context using integrated nested laplace approximation methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using PA and PO data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. 

Published paper available here: 

Morera-Pujol et al. 2023

We report on an observational before-and-after study on the association between forest clearfelling and bovine tuberculosis (bTB) herd risk in cattle herds, an episystem where badgers (Meles meles) are the primary wildlife spillover host. The study design compared herd bTB breakdown risk for a period of 1 year prior to and after exposure to clearfelling across Ireland at sites cut in 2015–2017 

Published paper available here: 

Byrne et al. 2022

We aimed to examine the link between ecological disturbance and relative bTB risk using Ireland as a case study. We analysed clearfell forestry operations and assessed bTB breakdowns within cattle farms across different spatio-temporal scales over multiple years, examining how ecological conditions may modulate this relationship using conditional logistic regression models.

Published paper available here: 

Murphy et al. 2022

In Ireland, international-standard camera trap surveys have not been undertaken to inform the management of large terrestrial wildlife. Through participation in a continent-wide initiative (Snapshot Europe) with a shared methodology, we undertook Ireland’s first systematic camera trap survey for large mammals over a two-month period in 2021 in a known deer “hotspot”-central Wicklow, Ireland.  

Published paper available here:
Smith et al. 2022

Autoethnography is a form of structured reflection whereby researchers use personal experience to contribute to understanding collaborative processes. We propose an applied form of autoethnography as a repeatable protocol to describe inter-organisational interactions during the research process in ecology and environmental research. We demonstrate the use of this protocol with five case studies from a diversity of wildlife research across a wide variety of experience levels and scales from small mammals, large herbivores and predators to digital ecology. Our applied autoethnography protocol would ensure that specific biases and context are adequately described and that problems encountered and lessons learned from the experience are reflected upon.

Published paper available here:

Murphy et al. 2022

In this paper, we discuss agent-based modeling as an open-source, accessible, and inclusive resource to substitute for lost fieldwork during COVID-19 and for future scenarios of travel restrictions such as climate change and economic downturn. We describe the benefits of Agent-Based models as a teaching and training resource for students across education levels. We discuss how and why educators and research scientists can implement them with examples from the literature on how agent-based models can be applied broadly across life science research.  

Published paper available here: 

Murphy et al. 2020