Potential impacts of climate change on agriculture and food safety within the island of Ireland

Climate and other environmental change presents a number of challenges for effective food safety. Food production, distribution and consumption takes place within functioning ecosystems but this backdrop is often ignored or treated as static and unchanging. The risks presented by environmental change include novel pests and diseases, often caused by problem species expanding their spatial distributions as they track changing conditions, toxin generation in crops, direct effects on crop and animal production, consequences for trade networks driven by shifting economic viability of production methods in changing environments and finally, wholesale transformation of ecosystems as they respond to novel climatic regimes.

Distribution of Childhood Health Indices: Associations with Moves along the "Property Ladder"

Background
Childhood deprivation is a major risk to public health. Poor health in the early years accumulates and is expressed in adult health inequalities. The importance of social mobility - moves into and out of poverty or, indeed, change in relative affluence - for child wellbeing is less well understood. Home ownership and house value may serve as a useful measure of relative affluence and deprivation.
Method
Analysis of the Northern Ireland Longitudinal Study dataset focused on cohort members aged 18 and under at the 2001 census and their families. Using housing tenure and house value reported in 2001 and 2011, moves along the “housing ladder” over ten years were identified. Outcome measures were physical disability and mental health status as reported in 2011. Logistic regression models tested if health outcomes varied by upward and downward changes in house value.
Results
After controlling for variations in age, sex, general health and social class, mental health is worse among those who moved to a lower value house. Compared to ‘no change’, those moving from the upper quintile of house value into social renting accommodation were almost six times more likely to report poor mental health (OR 5.90 95% CI 4.52, 7.70). Conversely, those experiencing the greatest upward movement were half as likely to report poor mental health (OR 0.46 95% CI 0.31, 0.68). There were smaller associations between physical health and downward (OR 2.66 95% CI 2.16, 3.27), and upward (OR 0.75 95% CI 0.61, 0.92) moves.
Conclusion
Poor mental health is more strongly associated with declines in living standards than with improvements. The gradient appears at multiple points along this proxy affluence-deprivation spectrum, not only at the extremes. Further research should explore whether circumstances surrounding moves, or change in social position explains the differential association between the health correlates of upward versus downward mobility.

Modelling the influence of biotic factors on species distribution patterns

Biotic interactions can have large effects on species distributions yet their role in shaping species ranges is seldom explored due to historical difficulties in incorporating biotic factors into models without a priori knowledge on interspecific interactions. Improved SDMs, which account for biotic factors and do not require a priori knowledge on species interactions, are needed to fully understand species distributions. Here, we model the influence of abiotic and biotic factors on species distribution patterns and explore the robustness of distributions under future climate change. We fit hierarchical spatial models using Integrated Nested Laplace Approximation (INLA) for lagomorph species throughout Europe and test the predictive ability of models containing only abiotic factors against models containing abiotic and biotic factors. We account for residual spatial autocorrelation using a conditional autoregressive (CAR) model. Model outputs are used to estimate areas in which abiotic and biotic factors determine species’ ranges. INLA models containing both abiotic and biotic factors had substantially better predictive ability than models containing abiotic factors only, for all but one of the four species. In models containing abiotic and biotic factors, both appeared equally important as determinants of lagomorph ranges, but the influences were spatially heterogeneous. Parts of widespread lagomorph ranges highly influenced by biotic factors will be less robust to future changes in climate, whereas parts of more localised species ranges highly influenced by the environment may be less robust to future climate. SDMs that do not explicitly include biotic factors are potentially misleading and omit a very important source of variation. For the field of species distribution modelling to advance, biotic factors must be taken into account in order to improve the reliability of predicting species distribution patterns both presently and under future climate change.