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Researchers in ecology and evolutionary biology are increasingly dependent on computational code to conduct research. Hence, the use of efficient methods to share, reproduce, and collaborate on code as well as document research is fundamental. GitHub is an online, cloud-based service that can help researchers track, organize, discuss, share, and collaborate on software and other materials related to research production, including data, code for analyses, and protocols. Despite these benefits, the use of GitHub in ecology and evolution is not widespread. To help researchers in ecology and evolution adopt useful features from GitHub to improve their research workflows, we review 12 practical ways to use the platform. We outline features ranging from low to high technical difficulty, including storing code, managing projects, coding collaboratively, conducting peer review, writing a manuscript, and using automated and continuous integration to streamline analyses. Given that members of a research team may have different technical skills and responsibilities, we describe how the optimal use of GitHub features may vary among members of a research collaboration. As more ecologists and evolutionary biologists establish their workflows using GitHub, the field can continue to push the boundaries of collaborative, transparent, and open research.

The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.

Social behavior is an important driver of infection dynamics, though identifying the social interactions that foster infectious disease transmission is challenging. Here we examine how social behavior impacts disease transmission in Indo-Pacific bottlenose dolphins (Tursiops aduncus) using an easily identifiable skin disease and social network data. We analyzed tattoo skin disease (TSD) lesions based on photographs collected as part of a 34-year longitudinal study in relation to the sociality of T. aduncus using three metrics (degree, time spent socializing, and time in groups) and network structure, using the k -test. We show that calves with TSD in the second year of life associated more with TSD-positive individuals in the first year of life compared with calves that did not have TSD. Additionally, the network k -test showed that the social network links are epidemiologically relevant for transmission. However, degree, time spent in groups, and time spent socializing were not significantly different between infected and uninfected groups. Our findings indicate that association with infected individuals is predictive of an individual's risk for TSD and that the social association network can serve as a proxy for studying the epidemiology of skin diseases in bottlenose dolphins. [ABSTRACT FROM AUTHOR]

Activity budget data are essential for determining behavioral responses to physiological and ecological variables. Yet, few studies are available to investigate the robustness, accuracy, and biases of the methods used to estimate activity budgets for cetaceans. In this study, we compare activity budgets of 55 adult female bottlenose dolphins in Shark Bay, Australia derived from two methods: surveys (n = 6,903) and focal follows (n = 1,185, totaling 2,721 h of observation). Activity budgets estimated from survey data differed in all behavioral states compared to focal follow data. However, when controlling for temporal autocorrelation, only time spent socializing and time spent traveling remained disparate between the methods. To control for biases associated with assigning group-level behavior to individuals, we also compared survey and focal follow activity budgets for lone females. Here we found differences between methods in time spent foraging and traveling regardless of whether we controlled for temporal autocorrelation, which suggests detection biases likely play a role in explaining differences in activity budget estimates between the two methodologies. Our results suggest that surveys are less representative of individual-level activity budgets, and thus, when individual-level knowledge about behavior is needed, focal follows are preferred. [ABSTRACT FROM AUTHOR]

Research on sex biases in longevity in mammals often assumes that male investment in competition results in a female survival advantage that is constant throughout life. We use 35 years of longitudinal data on 1003 wild bottlenose dolphins ( Tursiops aduncus ) to examine age-specific mortality, demonstrating a time-varying effect of sex on mortality hazard over the five-decade lifespan of a social mammal. Males are at higher risk of mortality than females during the juvenile period, but the gap between male and female mortality hazard closes in the mid-teens, coincident with the onset of female reproduction. Female mortality hazard is non-significantly higher than male mortality hazard in adulthood, resulting in a moderate male bias in the oldest age class. Bottlenose dolphins have an intensely male-competitive mating system, and juvenile male mortality has been linked to social competition. Contrary to predictions from sexual selection theory, however, male-male competition does not result in sustained male-biased mortality. As female dolphins experience high costs of sexual coercion in addition to long and energetically expensive periods of gestation and lactation, this suggests that substantial female investment in reproduction can elevate female mortality risk and impact sex biases in lifespan.

The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.