Abstract: There has been relatively little research on the psychological mechanisms of social learning. This may be due, in part, to the practice of distinguishing categories of social learning in relation to ill-defined mechanisms (Davis, 1973; Galef, 1988). This practice both makes it difficult to identify empirically examples of different types of social learning, and gives the false impression that the mechanisms responsible for social learning are clearly understood. It has been proposed that social learning phenomena be subsumed within the categorization scheme currently used by investigators of asocial learning. This scheme distinguishes categories of learning according to observable conditions, namely, the type of experience that gives rise to a change in an animal (single stimulus vs. stimulus-stimulus relationship vs. response-reinforcer relationship), and the type of behaviour in which this change is detected (response evocation vs. learnability) (Rescorla, 1988). Specifically, three alignments have been proposed: (i) stimulus enhancement with single stimulus learning, (ii) observational conditioning with stimulus-stimulus learning, or Pavlovian conditioning, and (iii) observational learning with response-reinforcer learning, or instrumental conditioning. If, as the proposed alignments suggest, the conditions of social and asocial learning are the same, there is some reason to believe that the mechanisms underlying the two sets of phenomena are also the same. This is so if one makes the relatively uncontroversial assumption that phenomena which occur under similar conditions tend to be controlled by similar mechanisms. However, the proposed alignments are intended to be a set of hypotheses, rather than conclusions, about the mechanisms of social learning; as a basis for further research in which animal learning theory is applied to social learning. A concerted attempt to apply animal learning theory to social learning, to find out whether the same mechanisms are responsible for social and asocial learning, could lead both to refinements of the general theory, and to a better understanding of the mechanisms of social learning. There are precedents for these positive developments in research applying animal learning theory to food aversion learning (e.g. Domjan, 1983; Rozin & Schull, 1988) and imprinting (e.g. Bolhuis, de Vox & Kruit, 1990; Hollis, ten Cate & Bateson, 1991). Like social learning, these phenomena almost certainly play distinctive roles in the antogeny of adaptive behaviour, and they are customarily regarded as 'special kinds' of learning (Shettleworth, 1993).(ABSTRACT TRUNCATED AT 400 WORDS)

Abstract: Since the first sporadic occurrences of grey wolves (Canis lupus) west of the Polish border in 1996, wolves have shown a rapid population recovery in Germany. Wolves are known to avoid people and wolf attacks on humans are very rare worldwide. However, the subjectively perceived threat is considerable, especially as food-conditioned habituation to humans occurs sporadically. Lower Saxony (Germany) has an exceedingly higher human population density than most other regions with territorial wolves; thus, the potential for human-wolf conflicts is higher. Using hunters' wildlife survey data from 455 municipalities and two years (2014-2015) and data from the official wolf monitoring (557 confirmed wolf presences and 500 background points) collected between 2012-2015, grey wolf habitat selection was modelled using generalized additive models with respect to human population density, road density, forest cover and roe deer density. Moreover, we tested whether habitat use changed in response to human population and road density between 2012/2013 and 2014/2015. Wolves showed a preference for areas of low road density. Human population density was less important as a covariate in the model of the survey data. Areas with higher prey abundance (5-10 roe deer/km2) and areas with >20% forest cover were preferred wolf habitats. Wolves were mostly restricted to areas with the lowest road and human population densities. However, between the two time periods, avoidance of human density decreased significantly. Recolonization of Germany is still in its early stages and it is unclear where this process will halt. To-date authorities mainly concentrate on monitoring measures. However, to avoid conflict, recolonization will require more stringent management of wolf populations and an improved information strategy for rural populations.

Abstract: Generalist predators are expected to shape their diets according to the local availability of prey species. In turn, the extent of consumption of a prey would be influenced by the number of alternative prey species. We have tested this prediction by considering the wild boar and the grey wolf: two widespread species whose distribution ranges overlap largely in Southern Europe, e.g. in Italy. We have reviewed 16 studies from a total of 21 study areas, to assess whether the absolute frequency of occurrence of wild boar in the wolf diet was influenced by (i) occurrence of the other ungulate species in diet and (ii) the number of available ungulate species. Wild boar turned out to be the main prey of the wolf (49% occurrence, on average), followed by roe deer (24%) and livestock (18%). Occurrence of wild boar in the wolf diet decreased with increasing usage of roe deer, livestock, and to a lower extent, chamois and red deer. The number of prey species did not influence the occurrence of wild boar in the wolf diet. The wild boar is a gregarious, noisy and often locally abundant ungulate, thus easily detectable, to a predator. In turn, the extent of predation on this ungulate may not be influenced so much by the availability of other potential prey. Heavy artificial reductions of wild boar numbers, e.g. through numerical control, may concentrate predation by wolves on alternative prey (e.g. roe deer) and/or livestock, thus increasing conflicts with human activities.

Abstract: In the USA, each year, up to one billion birds are estimated to die from colliding with windowpanes (Sabo et al. 2016). A further 573,000 are struck down by wind turbines, along with 888,000 bats (Smallwood 2013). Worldwide, unintended capture in fishing devices is recognized as the single most serious global threat to migratory, long-lived marine taxa including turtles, birds, mammals and sharks (Wallace et al. 2013). Estimates put the number of amphibians killed per year on Australian roads at 5 million (Seiler 2003). The likelihood of a green turtle erroneously ingesting plastic debris, often by mistaking them for food, rose from 30% in 1985 to almost 50% in 2012 (Schuyler et al. 2013). Human-induced rapid environmental change (HIREC, sensu Sih et al. 2011) is filling animals’ environments with new threats which bear little or excessive similarity to those they have encountered in their evolutionary history (Dwernychuk and Boag 1972; Patten and Kelley 2010; Witherington 1997). As a consequence, many of the stimuli involved fall outside the adaptive processing space of animals’ evolutionary perceptual, learning, memory and decision-making systems, making individuals particularly vulnerable to their impact.