Abstract: Most horses are trained by negative reinforcement. Currently, however, no standardised test for evaluating horses' negative reinforcement learning ability is available. The aim of this study was to develop an objective test to investigate negative reinforcement learning in horses. Twenty-four Icelandic horses (3 years old) were included in this study. The horses were tested in a pressure-release task on three separate days with 10, 7 and 5 trials on each side, respectively. Each trial consisted of pressure being applied on the hindquarter with an algometer. The force of the pressure was increased until the horse moved laterally away from the point of pressure. There was a significant decrease in required force over trials on the first test day (P<0.001), but not the second and third day. The intercepts on days 2 and 3 differed significantly from day 1 (P<0.001), but not each other. Significantly stronger force was required on the right side compared to the left (P<0.001), but there was no difference between first and second side tested (P=0.56). Individual performance was evaluated by median-force and the change in force over trials on the first test day. These two measures may explain different characteristics of negative reinforcement learning. In conclusion, this study presents a novel, standardised test for evaluating negative reinforcement learning ability in horses.

Abstract: Summary Reliable assessment of animal populations is a long-standing challenge in wildlife ecology. Technological advances have led to widespread adoption of camera traps (CTs) to survey wildlife distribution, abundance and behaviour. As for any wildlife survey method, camera trapping must contend with sources of sampling error such as imperfect detection. Early applications focused on density estimation of naturally marked species, but there is growing interest in broad-scale CT surveys of unmarked populations and communities. Nevertheless, inferences based on detection indices are controversial, and the suitability of alternatives such as occupancy estimation is debatable. We reviewed 266 CT studies published between 2008 and 2013. We recorded study objectives and methodologies, evaluating the consistency of CT protocols and sampling designs, the extent to which CT surveys considered sampling error, and the linkages between analytical assumptions and species ecology. Nearly two-thirds of studies surveyed more than one species, and a majority used response variables that ignored imperfect detection (e.g. presence?absence, relative abundance). Many studies used opportunistic sampling and did not explicitly report details of sampling design and camera deployment that could affect conclusions. Most studies estimating density used capture?recapture methods on marked species, with spatially explicit methods becoming more prominent. Few studies estimated density for unmarked species, focusing instead on occupancy modelling or measures of relative abundance. While occupancy studies estimated detectability, most did not explicitly define key components of the modelling framework (e.g. a site) or discuss potential violations of model assumptions (e.g. site closure). Studies using relative abundance relied on assumptions of equal detectability, and most did not explicitly define expected relationships between measured responses and underlying ecological processes (e.g. animal abundance and movement). Synthesis and applications. The rapid adoption of camera traps represents an exciting transition in wildlife survey methodology. We remain optimistic about the technology's promise, but call for more explicit consideration of underlying processes of animal abundance, movement and detection by cameras, including more thorough reporting of methodological details and assumptions. Such transparency will facilitate efforts to evaluate and improve the reliability of camera trap surveys, ultimately leading to stronger inferences and helping to meet modern needs for effective ecological inquiry and biodiversity monitoring.

Abstract: The evaluation of important parameters for measuring the horses’ cognitive capacities is one of the central topics of the equine behaviour team at Nürtingen-Geislingen University. Social complexity has been said to be one of the settings in which needs for cognitive capacities arise in animals. A variety of studies throughout the last two decades proved the horses’ social complexity to be far more elaborate than previously assumed. Horses form social bonds for the protection of offspring, intervene in encounters of others, identify group mates individually and easily orientate in a fission fusion society.
In such socially complex societies, animals will benefit from learning socially. In many bird and primate species the degree of social complexity correlates nicely with the species abilities for social learning. Social learning was, therefore, argued to be an indicator for elaborate mental capacities in animals. We were delighted to prove that horses actually copy social behaviour and techniques for operating a feeding apparatus from older and higher ranking group members. In a recent study we found young horses, at the age of 3 to 12, to copy the operation of a feeding apparatus from a human demonstrator. Social learning seems to work nicely in horses when the social background of the animals is considered.
The degree to which individual animals adapt to changes in their social or physical environment by finding innovative solution appears to be the other side of the coin, of whether animals adjust to challenges by social learning. It is not very astonishing, that along with the animals’ social complexity and their ability to learn socially also the degree to which they show innovative behaviour was claimed to be one of the most important demonstrations of advanced cognitive capacities. In a recent approach, we started to ask horse owners and horse keepers in many countries to tell us about unusual behaviour of their horses via a web site (http://innovative-behaviour.org). To date, we received 204 cases of innovative behaviour descriptions from which six cases were clear examples of tool use or borderline tool use. We categorized the innovative behaviours into the classes, a) innovations to gain food, b) innovations to gain freedom, c) social innovations, d) innovations to increase maintenance, and e) innovations that could not be clearly assigned to a category. About 20% of the innovative horses showed more than one innovation. These animals could be termed “true innovators”. Again, young horses were more innovative than older ones with the age group 5 – 9 showing the highest number of innovative behaviour descriptions.
In a nutshell, the horses’ cognitive capacities appear to be underestimated throughout the last decades. The horses’ social complexity is far more elaborate than previously assumed, horses learn socially from conspecific and humans, some of them demonstrate innovative behaviour adaptations to their environment and even simple forms of tool use.