Political affinity and investors' response to the acquisition premium in cross‐border M&A transactions — A moderation analysis

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: The data that support the findings of this study are available from WRDS/SDC Platinum/Datastream. Restrictions apply to the availability of these data, which were used under license for this study. Data are available via WRDS/SDC Platinum/Datastream.This article investigates the moderating effect of political affinity between countries on investors' reactions to the premium in cross-border acquisitions (CBAs). Based on a sample of 1,183 CBAs between 1999 and 2018, we find that political affinity positively moderates the relationship between the acquisition premium and the acquiring and target firms' stock market return. We argue that investors use political affinity to assess the reliability of the premium (i.e., management's overall perception of a given deal's synergistic potential). This is in line with prior literature reasoning that, unlike strong political affinity, weak political affinity increases the likelihood of government intervention, decreases the likelihood of deal completion, and results in higher premiums to mitigate the previous effects, thus potentially increasing the likelihood of value destruction

Importance of resource selection and social behavior to partitioning of hostile space by sympatric canids

Investigations into mechanisms of resource partitioning are particularly suited to systems where nascent interactive behaviors are observable. Wolf (Canis lupus) recolonization of the Greater Yellowstone Ecosystem provided such a system, and we were able to identify behaviors influencing the partitioning of resources by coyotes (Canis latrans) and wolves. We observed coyote–wolf interactions immediately after wolf recolonization, when reemergent behaviors mediating the outcome of competitive interactions were detectable and mechanisms of spatial avoidance were identifiable. Although coyotes used the same space as wolves, they likely minimized risk of encounter by making adaptive changes in resource selection based on perception of wolf activity and potential scavenging opportunities. When exploiting carrion subsidies (i.e., wolf-killed ungulates), coyotes relied on social behaviors (i.e., numerical advantage in concert with heightened aggression) to mitigate escalating risk from wolves and increase resource-holding potential. By adapting behaviors to fluctuating risk, coyotes might reduce the amplitude of competitive asymmetries. We concluded coyotes do not perceive wolves as a threat requiring generalized spatial avoidance. Rather, the threat of aggressive interactions with wolves is spatially discrete and primarily contained to areas adjacent to carrion resources

The Effect of Human Immunodeficiency Virus on Hepatitis B Virus Serologic Status in Co-Infected Adults

Factors associated with serologic hepatitis B virus (HBV) outcomes in HIV-infected individuals remain incompletely understood, yet such knowledge may lead to improvements in the prevention and treatment of chronic HBV infection.HBV-HIV co-infected cohort participants were retrospectively analyzed. HBV serologic outcomes were classified as chronic, resolved, and isolated-HBcAb. Chronic HBV (CHBV) was defined as the presence of HBsAg on two or more occasions at least six months apart. Risk factors for HBV serologic outcome were assessed using logistic regression. Of 2037 participants with HBV infection, 281 (14%) had CHBV. Overall the proportions of HBV infections classified as CHBV were 11%, 16%, and 19% for CD4 cell count strata of > or =500, 200-499, and <200, respectively (p<0.0001). Risk of CHBV was increased for those with HBV infection occurring after HIV diagnosis (OR 2.62; 95% CI 1.78-3.85). This included the subset with CD4 count > or =500 cells/microL where 21% of those with HBV after HIV diagnosis had CHBV compared with 9% for all other cases of HBV infection in this stratum (p = 0.0004). Prior receipt of HAART was associated with improved HBV serologic outcome overall (p = 0.012), and specifically among those with HBV after HIV (p = 0.002). In those with HBV after HIV, HAART was associated with reduced risk of CHBV overall (OR 0.18; 95% CI 0.04-0.79); including reduced risk in the subsets with CD4 > or =350 cells/microL (p<0.001) and CD4 > or =500 cells/microL (p = 0.01) where no cases of CHBV were seen in those with a recent history of HAART use.Clinical indicators of immunologic status in HIV-infected individuals, such as CD4 cell count, are associated with HBV serologic outcome. These data suggest that immunologic preservation through the increased use of HAART to improve functional anti-HBV immunity, whether by improved access to care or earlier initiation of therapy, would likely improve HBV infection outcomes in HIV-infected individuals

An evaluation of platforms for processing camera-trap data using artificial intelligence

Camera traps have quickly transformed the way in which many ecologists study the distribution of wildlife species, their activity patterns and interactions among members of the same ecological community. Although they provide a cost-effective method for monitoring multiple species over large spatial and temporal scales, the time required to process the data can limit the efficiency of camera-trap surveys. Thus, there has been considerable attention given to the use of artificial intelligence (AI), specifically deep learning, to help process camera-trap data. Using deep learning for these applications involves training algorithms, such as convolutional neural networks (CNNs), to use particular features in the camera-trap images to automatically detect objects (e.g. animals, humans, vehicles) and to classify species. To help overcome the technical challenges associated with training CNNs, several research communities have recently developed platforms that incorporate deep learning in easy-to-use interfaces. We review key characteristics of four AI platforms—Conservation AI, MegaDetector, MLWIC2: Machine Learning for Wildlife Image Classification and Wildlife Insights—and two auxiliary platforms—Camelot and Timelapse—that incorporate AI output for processing camera-trap data. We compare their software and programming requirements, AI features, data management tools and output format. We also provide R code and data from our own work to demonstrate how users can evaluate model performance. We found that species classifications from Conservation AI, MLWIC2 and Wildlife Insights generally had low to moderate recall. Yet, the precision for some species and higher taxonomic groups was high, and MegaDetector and MLWIC2 had high precision and recall when classifying images as either ‘blank’ or ‘animal’. These results suggest that most users will need to review AI predictions, but that AI platforms can improve efficiency of camera-trap-data processing by allowing users to filter their dataset into subsets (e.g. of certain taxonomic groups or blanks) that can be verified using bulk actions. By reviewing features of popular AI-powered platforms and sharing an open-source GitBook that illustrates how to manage AI output to evaluate model performance, we hope to facilitate ecologists' use of AI to process camera-trap data

Invasion speeds for structured populations in fluctuating environments

We live in a time where climate models predict future increases in environmental variability and biological invasions are becoming increasingly frequent. A key to developing effective responses to biological invasions in increasingly variable environments will be estimates of their rates of spatial spread and the associated uncertainty of these estimates. Using stochastic, stage-structured, integro-difference equation models, we show analytically that invasion speeds are asymptotically normally distributed with a variance that decreases in time. We apply our methods to a simple juvenile-adult model with stochastic variation in reproduction and an illustrative example with published data for the perennial herb, \emph{Calathea ovandensis}. These examples buttressed by additional analysis reveal that increased variability in vital rates simultaneously slow down invasions yet generate greater uncertainty about rates of spatial spread. Moreover, while temporal autocorrelations in vital rates inflate variability in invasion speeds, the effect of these autocorrelations on the average invasion speed can be positive or negative depending on life history traits and how well vital rates ``remember'' the past

Long-term Site Fidelity and Individual Home Range Shifts in Lophocebus albigena

We investigated long-term site fidelity of gray-cheeked mangabey (Lophocebus albigena) groups in Kibale National Park, Uganda. Concurrently, we monitored shifts in home range by individual females and subadult and adult males. We documented home range stability by calculating the area of overlap in successive years, and by recording the drift of each group’s monthly centroid from its initial location. Home ranges remained stable for 3 of our 4 groups (overlap over 10 yr >60%). Core areas were more labile, but group centroids drifted an average of only 530 m over the entire decade. Deviations from site fidelity were associated with dispersal or group fission. During natal dispersal, subadult males expanded their home ranges over many months, settling ≤4 home ranges away. Adult males, in contrast, typically dispersed within a few days to an adjacent group in an area of home range overlap. Adult males made solitary forays, but nearly always into areas used by their current group or by a group to which they had previously belonged. After secondary dispersal, they expanded their ranging in the company of their new group, apparently without prior solitary exploration of the new area. Some females also participated in home range shifts. Females shifted home ranges only within social groups, in association with temporary or permanent group splits. Our observations raise the possibility that male mangabeys use a finder-joiner mechanism when moving into new home ranges during secondary dispersal. Similarly, females might learn new resource locations from male immigrants before or during group fission

Animal Interactions and the Emergence of Territoriality

Inferring the role of interactions in territorial animals relies upon accurate recordings of the behaviour of neighbouring individuals. Such accurate recordings are rarely available from field studies. As a result, quantification of the interaction mechanisms has often relied upon theoretical approaches, which hitherto have been limited to comparisons of macroscopic population-level predictions from un-tested interaction models. Here we present a quantitative framework that possesses a microscopic testable hypothesis on the mechanism of conspecific avoidance mediated by olfactory signals in the form of scent marks. We find that the key parameters controlling territoriality are two: the average territory size, i.e. the inverse of the population density, and the time span during which animal scent marks remain active. Since permanent monitoring of a territorial border is not possible, scent marks need to function in the temporary absence of the resident. As chemical signals carried by the scent only last a finite amount of time, each animal needs to revisit territorial boundaries frequently and refresh its own scent marks in order to deter possible intruders. The size of the territory an animal can maintain is thus proportional to the time necessary for an animal to move between its own territorial boundaries. By using an agent-based model to take into account the possible spatio-temporal movement trajectories of individual animals, we show that the emerging territories are the result of a form of collective animal movement where, different to shoaling, flocking or herding, interactions are highly heterogeneous in space and time. The applicability of our hypothesis has been tested with a prototypical territorial animal, the red fox (Vulpes vulpes)

A Low-Cost GPS GSM/GPRS Telemetry System: Performance in Stationary Field Tests and Preliminary Data on Wild Otters (Lutra lutra)

Background: Despite the increasing worldwide use of global positioning system (GPS) telemetry in wildlife research, it has never been tested on any freshwater diving animal or in the peculiar conditions of the riparian habitat, despite this latter being one of the most important habitat types for many animal taxa. Moreover, in most cases, the GPS devices used have been commercial and expensive, limiting their use in low-budget projects. Methodology/Principal Findings: We have developed a low-cost, easily constructed GPS GSM/GPRS (Global System for Mobile Communications/General Packet Radio Service) and examined its performance in stationary tests, by assessing the influence of different habitat types, including the riparian, as well as water submersion and certain climatic and environmental variables on GPS fix-success rate and accuracy. We then tested the GPS on wild diving animals, applying it, for the first time, to an otter species (Lutra lutra). The rate of locations acquired during the stationary tests reached 63.2%, with an average location error of 8.94 m (SD = 8.55). GPS performance in riparian habitats was principally affected by water submersion and secondarily by GPS inclination and position within the riverbed. Temporal and spatial correlations of location estimates accounted for some variation in the data sets. GPS-tagged otters also provided accurate locations and an even higher GPS fix-success rate (68.2%). Conclusions/Significance: Our results suggest that GPS telemetry is reliably applicable to riparian and even divin

Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns

Increased availability of high-resolution movement data has led to the development of numerous methods for studying changes in animal movement behavior. Path segmentation methods provide basics for detecting movement changes and the behavioral mechanisms driving them. However, available path segmentation methods differ vastly with respect to underlying statistical assumptions and output produced. Consequently, it is currently difficult for researchers new to path segmentation to gain an overview of the different methods, and choose one that is appropriate for their data and research questions. Here, we provide an overview of different methods for segmenting movement paths according to potential changes in underlying behavior. To structure our overview, we outline three broad types of research questions that are commonly addressed through path segmentation: 1) the quantitative description of movement patterns, 2) the detection of significant change-points, and 3) the identification of underlying processes or ‘hidden states’. We discuss advantages and limitations of different approaches for addressing these research questions using path-level movement data, and present general guidelines for choosing methods based on data characteristics and questions. Our overview illustrates the large diversity of available path segmentation approaches, highlights the need for studies that compare the utility of different methods, and identifies opportunities for future developments in path-level data analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40462-016-0086-5) contains supplementary material, which is available to authorized users