Investigation of humans individual differences as predictors of their animal interaction styles, focused on the domestic cat.

Humans' individual differences including their demographics, personality, attitudes and experiences are often associated with important outcomes for the animals they interact with. This is pertinent to companion animals such as cats and dogs, given their social and emotional importance to humans and degree of integration into human society. However, the mechanistic underpinnings and causal relationships that characterise links between human individual differences and companion animal behaviour and wellbeing are not well understood. In this exploratory investigation, we firstly quantified the underlying structure of, and variation in, human's styles of behaviour during typical human-cat interactions (HCI), focusing on aspects of handling and interaction known to be preferred by cats (i.e. 'best practice'), and their variation. We then explored the potential significance of various human individual differences as predictors of these HCI styles. Seven separate HCI styles were identified via Principal Component Analysis (PCA) from averaged observations for 119 participants, interacting with sociable domestic cats within a rehoming context. Using General Linear Models (GLMs) and an Information Theoretic (IT) approach, we found these HCI PC components were weakly to strongly predicted by factors including cat-ownership history, participant personality (measured via the Big Five Inventory, or BFI), age, work experience with animals and participants' subjective ratings of their cat behaviour knowledge. Paradoxically, greater cat ownership experiences and self-assessed cat knowledge were not positively associated with 'best practice' styles of HCI, but were instead generally predictive of HCI styles known to be less preferred by cats, as was greater participant age and Neuroticism. These findings have important implications regarding the quality of human-companion animal relationships and dyadic compatibility, in addition to the role of educational interventions and their targeting for optimal efficacy. In the context of animal adoption, these results strengthen the (limited) evidence base for decision making associated with cat-adopter screening and matching. In particular, our results suggest that greater cat ownership experiences and self-reports of cat knowledge might not necessarily convey advantages for cats in the context of HCI.

Gene flow in commercial alfalfa (Medicago sativa subsp. sativa L.) seed production fields: Distance is the primary but not the sole influence on adventitious presence.

In insect-pollinated crops, gene flow is affected by numerous factors including crop characteristics, mating system, life history, pollinators, and planting management practices. Previous studies have concentrated on the impact of distance between genetically engineered (GE) and conventional fields on adventitious presence (AP) which represents the unwanted presence of a GE gene. Variables other than distance, however, may affect AP. In addition, some AP is often present in the parent seed lots used to establish conventional fields. To identify variables that influence the proportion of AP in conventional alfalfa fields, we performed variable selection regression analyses. Analyses based on a sample-level and a field-level analysis gave similar, though not identical results. For the sample-level model, distance from the GE field explained 66% of the variance in AP, confirming its importance in affecting AP. The area of GE fields within the pollinator foraging range explained an additional 30% of the variation in AP in the model. The density of alfalfa leafcutting bee domiciles influenced AP in both models. To minimize AP in conventional alfalfa seed fields, management practices should focus on optimizing isolation distances while also considering the size of the GE pollen pool within the pollinator foraging range, and the foraging behavior of pollinators.