Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high‐quality presence–absence (PA) data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low‐quality presence‐only (PO) data collected by citizen scientists, opportunistic observations and culling returns for game species. 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. Model predictions were associated to spatial estimate of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species‐specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.

A portable, low cost sensing system is described which interfaces to an electronic tongue sensor. The sensor used is a voltammetric sensor which monitors electrochemical reactions that occur in solutions. The sensor is able to test a range of liquids with different electrochemical properties without any hardware adjustments to the system. The system can automatically adjust for the change in solution properties by performing a routine which uses an auto-ranging feature to determine a current-to-voltage conversion of the sensor data by using a binary search strategy. This eliminates the intervention of the user to modify the system each time a new solution is tested. The effectiveness of the calibration routine was tested by carrying out cyclic voltammetry in two different solutions, 0.1M sulfuric acid solution and the phosphate buffered solution of pH3. The sensor system was able to accurately acquire the sensor data for each solution.

Fieldwork encompasses any practical work taking place outside the laboratory for data collection and learning (Lock, 1998). Field data collection is essential to investigate long-term ecological processes and observe new phenomena for the first time. Moreover, university fieldwork is a central component of coursework as fieldwork-based skills including project design, surveying, data curation, and risk assessment are vital for students seeking work in a competitive job market (Pool & Sewell, 2007). Thus, fieldwork for research and teaching purposes provides essential training and experience for early-career researchers (Peacock & Bacon, 2018).

1. Human modification of landscapes and associated disturbances may facilitate the emergence and spread of zoonotic diseases. Policy-makers need better understanding of the link between anthropogenic disturbances and wildlife disease hosts at the interface of human society and the natural environment, for example agriculture, forestry and aquaculture. Empirical research is strongly

Applied research involves interactions between different organisations—academia, industry, government. Breakdowns in communication can occur during these interactions which alter a project's outcome. We omit how we encounter and overcome these problems from scientific manuscripts which mask the social and cultural considerations that are critical to a project's success. 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. These reports can be presented as stand‐alone publications where appropriate, that is, to communicate an effective solution for a novel problem, or within the methods or supplementary material of manuscripts to further explain how the project developed from initial idea to final publication. Furthermore, this protocol can be used by practitioners to evaluate the trajectory of management decisions and policy implications in their jurisdiction to promote transparency and improve communication with stakeholders. Synthesis and Applications: Applied science will continue to intersect with organisations that help or hinder research efforts depending on cultural contexts and biases. Using adequate reflection on case studies to record these experiences and disseminate lessons to the wider community will improve how we approach problems in research, help us to avoid repeating mistakes and ultimately save time and resources. Outside of research, case studies derived from this protocol allow practitioners to holistically understand the methods, biases and challenges of the research from a new perspective, thus providing a novel knowledge brokering function between academia and practitioners in applied ecology.

The use of georeferenced information on the presence of a species to predict its distribution across a geographic area is one of the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have more abundant low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually less abundant and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive and abundant presence-only data. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. These models, however, have only been applied to simulated data so far. Here, for the first time, we apply this approach to empirical data, using presence-absence and presence-only data for the three main 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. Models' predictions were associated to spatial estimate of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we validated the three species-specific models using independent deer hunting returns. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unused and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.

Disturbance ecology refers to the study of discrete processes that disrupt the structure or dynamics of an ecosystem. Such processes can, therefore, affect wildlife species ecology, including those that are important pathogen hosts. 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. The percent of herds positive rose from 3.47% prior to clearfelling to 4.08% after exposure. After controlling for confounders (e.g., herd size, herd type), we found that cattle herds significantly increased their odds of experiencing a bTB breakdown by 1.2-times (95%CIs: 1.07–1.36) up to 1 year after a clearfell risk period. Disturbance ecology of wildlife reservoirs is an understudied area with regards to shared endemic pathogens. Epidemiological observational studies are the first step in building an evidence base to assess the impact of such disturbance events; however, such studies are limited in inferring the mechanism for any changes in risk observed. The current cohort study suggested an association between clearfelling and bTB risk, which we speculate could relate to wildlife disturbance affecting pathogen spillback to cattle, though the study design precludes causal inference. Further studies are required. However, ultimately, integration of epidemiology with wildlife ecology will be important for understanding the underlying mechanisms involved, and to derive suitable effective management proposals, if required.

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