Stuart Green

Population Estimation and Trappability of the European Badger (Meles meles): Implications for Tuberculosis Management

Estimates of population size and trappability inform vaccine efficacy modelling and are required for adaptive management during prolonged wildlife vaccination campaigns. We present an analysis of mark-recapture data from a badger vaccine (Bacille Calmette–Guérin) study in Ireland. This study is the largest scale (755 km2) mark-recapture study ever undertaken with this species. The study area was divided into three approximately equal–sized zones, each with similar survey and capture effort. A mean badger population size of 671 (SD: 76) was estimated using a closed-subpopulation model (CSpM) based on data from capturing sessions of the entire area and was consistent with a separate multiplicative model. Minimum number alive estimates calculated from the same data were on average 49–51% smaller than the CSpM estimates, but these are considered severely negatively biased when trappability is low. Population densities derived from the CSpM estimates were 0.82–1.06 badgers km−2, and broadly consistent with previous reports for an adjacent area. Mean trappability was estimated to be 34–35% per session across the population. By the fifth capture session, 79% of the adult badgers caught had been marked previously. Multivariable modelling suggested significant differences in badger trappability depending on zone, season and age-class. There were more putatively trap-wary badgers identified in the population than trap-happy badgers, but wariness was not related to individual’s sex, zone or season of capture. Live-trapping efficacy can vary significantly amongst sites, seasons, age, or personality, hence monitoring of trappability is recommended as part of an adaptive management regime during large–scale wildlife vaccination programs to counter biases and to improve efficiencies.

Assessing the Geographic Representativity of Farm Accountancy Data

The environment affects agriculture, via soils, weather, etc. and agriculture affects the environment locally at farm level and via its impact on climate change. Locating agriculture within its spatial environment is thus important for farmers and policy makers. Within the EU countries collect detailed farm data to understand the technical and financial performance of farms; the Farm Accountancy Data Network. However, knowledge of the spatial-environmental context of these farms is reported at gross scale. In this paper, Irish farm accounting data is geo-referenced using address matching to a national address database. An analysis of the geographic distribution of the survey farms, illustrated through a novel 2D ranked pair plot of the coordinates, compared to the national distribution of farms shows a trend in the location of survey farms that leads to a statistical difference in the climatic variables associated with the farm. The farms in the survey have significantly higher accumulated solar radiation values than the national average. As a result, the survey may not be representative spatially of the pattern of environment x farm system. This could have important considerations when using FADN data in modelling climate change impacts on agri-economic performance.

Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data—A Machine Learning Approach

More than 80% of agricultural land in Ireland is grassland, which is a major feed source for the pasture based dairy farming and livestock industry. Many studies have been undertaken globally to estimate grassland biomass by using satellite remote sensing data, but rarely in systems like Irelands intensively managed, but small-scale pastures, where grass is grazed as well as harvested for winter fodder. Multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed to estimate the grassland biomass (kg dry matter/ha/day) of two intensively managed grassland farms in Ireland. For the first test site (Moorepark) 12 years (2001–2012) and for second test site (Grange) 6 years (2001–2005, 2007) of in situ measurements (weekly measured biomass) were used for model development. Five vegetation indices plus two raw spectral bands (RED=red band, NIR=Near Infrared band) derived from an 8-day MODIS product (MOD09Q1) were used as an input for all three models. Model evaluation shows that the ANFIS (

Application of statistical and machine learning models for grassland yield estimation based on a hypertemporal satellite remote sensing time series

R_{{\rm{Moorepark}}}^2 = \;0.85,\;\;{\rm{RMS}}{{\rm{E}}_{{\rm{Moorepark}}}} = \;11.07

Upland vegetation mapping using Random Forests with optical and radar satellite data

;

The Irish Forest Soils Project and its Potential Contribution to the Assessment of Biodiversity

R_{{\rm{Grange}}}^2 = \;0.76,\;\;{\rm{RMS}}{{\rm{E}}_{{\rm{Grange}}}} = \;15.35

Predicted distribution of High Nature Value farmland in the Republic of Ireland

) has produced improved estimation of biomass as compared to the ANN and MLR. The proposed methodology will help to better explore the future inflow of remote sensing data from spaceborne sensors for the retrieval of different biophysical parameters, and with the launch of new members of satellite families (ALOS-2, Radarsat-2, Sentinel, TerraSAR-X, TanDEM-X/L) the development of tools to process large volumes of image data will become increasingly important.

Evaluation of multi-temporal and multi-sensor atmospheric correction strategies for land-cover accounting and monitoring in Ireland

More than 80% of agricultural land in Ireland is grassland, providing a major feed source for the pasture based dairy farming and livestock industry. Intensive grass based systems demand high levels of intervention by the farmer, with estimation of pasture cover (biomass) being the most important variable in land use management decisions, as well as playing a vital role in paddock and herd management. Many studies have been undertaken to estimate grassland biomass using satellite remote sensing data, but rarely in systems like Ire-lands intensively managed, small scale pastures, where grass is grazed as well as harvested for winter fodder. The objective of this study is to estimate grassland yield (kgDM/ha) from MODIS derived vegetation indices on a near weekly basis across the entire 300+ day growing season using three different methods (Multiple Linear Regression (MLR), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)). The results show that ANFIS model produced best result (R2 = 0.86) as compare to the ANN (R2 = 0.57) and MLR (R2 = 0.31).

Standardizing Terminology for Landscape Categorization: an Irish Agri-environment Perspective

Abstract Uplands represent unique landscapes that provide a range of vital benefits to society, but are under increasing pressure from the management needs of a diverse number of stakeholders (e.g. farmers, conservationists, foresters, government agencies and recreational users). Mapping the spatial distribution of upland vegetation could benefit management and conservation programmes and allow for the impacts of environmental change (natural and anthropogenic) in these areas to be reliably estimated. The aim of this study was to evaluate the use of medium spatial resolution optical and radar satellite data, together with ancillary soil and topographic data, for identifying and mapping upland vegetation using the Random Forests (RF) algorithm. Intensive field survey data collected at three study sites in Ireland as part of the National Parks and Wildlife Service (NPWS) funded survey of upland habitats was used in the calibration and validation of different RF models. Eight different datasets were analysed for each site to compare the change in classification accuracy depending on the input variables. The overall accuracy values varied from 59.8% to 94.3% across the three study locations and the inclusion of ancillary datasets containing information on the soil and elevation further improved the classification accuracies (between 5 and 27%, depending on the input classification dataset). The classification results were consistent across the three different study areas, confirming the applicability of the approach under different environmental contexts.

Simulation of soil carbon efflux from an arable soil using the ECOSSE model: Need for an improved model evaluation framework?

The United Nations Environment Programme (UNEP) has proposed methods and thematic areas for data collection that are appropriate to the evaluation of biodiversity. The Heritage Council has identified a paucity of data on habitats in Ireland. Within this context, we outline the Irish Forest Soils (IFS) element of the Forest Inventory and Planning System (FIPS) and present a detailed account of land-cover mapping, which is an important aspect of the project. The IFS project aims to produce a national thematic map of land cover using soft-copy photogrammetry, combined with satellite-image classification and field survey. This aspect of the IFS project generates data on land cover at different spatial and classification resolutions. We report on the progress made to date and present illustrative examples of the data sets. The UNEP proposals provide a useful framework within which to discuss the potential contribution of IFS data to the assessment of biodiversity.