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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.

Conservation priorities and legal protections are often based on confirmed species occurrences. However, imperfect detection is likely the norm in biological surveys, resulting in negative consequences for conservation. Focusing on threatened species in the US and Canada, we show that detectability information appears to be lacking for most species that are conservation priorities. Although more research on species detection is needed, detectability estimates are important for many immediate decisions. Thus, we recommend: (1) estimating and accounting for detectability and designing rigorous surveys when confirming presence or absence is crucial. Otherwise, absence in surveys should be considered suggestive only and critical habitat should be managed even if species presences are unconfirmed. (2) When directly estimating detectability is prohibitively difficult, indirect estimates should be explored, for example through expert elicitation or trait‐based predictors. (3) Detectability should be explicitly incorporated into decisions to ensure that surveys and management actions are directed where they have the greatest potential benefit. [ABSTRACT FROM AUTHOR]

Value of information (VoI) analysis is a method for quantifying how additional information may improve management decisions, with applications ranging from conservation to fisheries. However, VoI studies frequently suggest that collecting more data will not substantially improve management outcomes. This often contradicts the intuition of ecologists and managers who usually believe new information is critical for management. This inconsistency is exacerbated by the perception that VoI is a black‐box method. A lack of understanding as to why VoI is usually lower than ecologists expect is hampering on‐ground uptake. There is an urgent need to identify the factors that drive VoI methodology to produce low values.Here, we use a rigorous approach to provide insights into why VoI values are often low. We first derive analytic solutions and upper bounds for a VoI problem with two uncertain states, two actions, and four management outcomes. We show how VoI changes with respect to the benefit (i.e. utility) of implementing actions in each state, and the probability the system is in each state. We apply our formulation to a published frog population management case study and extend the results numerically to 10 million randomly generated larger‐sized problems.Zero VoI occurred half of the time in our two‐action two‐state simulations, corresponding to when one action is best, or equal best, across all states. Even when VoI values were positive, they were typically low. However, on average, VoI tended to increase with the number of states and actions. Our analytic expression for VoI, in the case where VoI is positive, demonstrates that VoI is characterized by the state probabilities and, the utility gaps, that is the difference in utility of deploying each action in each state. Our derived bounds reveal that, in all two‐action two‐state systems, VoI cannot be larger than half the largest utility gap.Our simple, yet powerful, analysis provides precious insight into the important factors that drive VoI analysis. Our work provides an essential stepping stone towards increasing the interpretability of VoI analysis in more complex settings, ultimately empowering managers to use VoI to help inform their decisions. [ABSTRACT FROM AUTHOR]

Population trend estimates form the core of avian conservation assessments in North America and indicate important changes in the state of the natural world. The models used to estimate these trends would be more efficient and informative for conservation if they explicitly considered the spatial locations of the monitoring data. We created spatially explicit versions of some standard status and trend models applied to long-term monitoring data for birds across North America. We compared the spatial models to simpler non-spatial versions of the same models, fitting them to simulated data and real data from 3 broad-scale monitoring programs: the North American Breeding Bird Survey (BBS), the Christmas Bird Count, and a collection of programs we refer to as Migrating Shorebird Surveys. All the models generally reproduced the simulated trends and population trajectories when there were many data, and the spatial models performed better when there were fewer data and in locations where the local trends differed from the range-wide means. When fit to real data, the spatial models revealed interesting spatial patterns in trend, such as recent population increases along the Appalachian Mountains for the Eastern Whip-poor-will (Antrostomus vociferus), that were much less apparent in results from the non-spatial versions. The spatial models also had higher out-of-sample predictive accuracy than the non-spatial models for a selection of species using BBS data. The spatially explicit sharing of information allows fitting the models with much smaller strata, allowing for finer-grained patterns in trends. Spatially informed trends will facilitate more locally relevant conservation, highlight areas of conservation successes and challenges, and help generate and test hypotheses about the spatially dependent drivers of population change. [ABSTRACT FROM AUTHOR]

The conversion of forest to agriculture is considered one of the greatest threats to avian biodiversity, yet how species respond to habitat modification throughout the annual cycle remains unknown. We examined whether forest bird associations with agricultural habitats vary throughout the year, and if species traits influence these relationships. Using data from the eBird community‐science program, we investigated associations between agriculturally‐modified land cover and the occurrence of 238 forest bird species based on three sets of avian traits: migratory strategy, dietary guild, and foraging strategy. We found that the influence of agriculturally‐modified land cover on species distributions varied widely across periods and trait groups but highlighting several broad findings. First, migratory species showed strong seasonal differences in their response to agricultural land cover while resident species did not. Second, there was a migratory strategy by season interaction; Neotropical migrants were most negatively influenced by agricultural land cover during the breeding period while short‐distance migrants were most negatively influenced during the non‐breeding period. Third, regardless of season, some dietary (e.g. insectivores) and foraging guilds (e.g. bark foragers) consistently responded more negatively to agricultural land cover than others (e.g. omnivores and ground foragers, respectively). Fourth, there were greater differences among dietary guilds in their responses to agricultural land cover during the breeding period than during the non‐breeding period, perhaps reflecting how different habitat and ecological requirements enhance the susceptibility of some guilds during reproduction. These results suggest that management efforts across the annual cycle may be oversimplified and thus ineffective when based on broad ecological generalisations that are static in space and time. [ABSTRACT FROM AUTHOR]

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