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Although the concept of connectivity is ubiquitous in ecology and evolution, its definition is often inconsistent, particularly in interdisciplinary research. In an ecological context, population connectivity refers to the movement of individuals or species across a landscape. It is measured by locating organisms and tracking their occurrence across space and time. In an evolutionary context, connectivity is typically used to describe levels of current and past gene flow, calculated from the degree of genetic similarity between populations. Both connectivity definitions are useful in their specific contexts, but rarely are these two perspectives combined. Different definitions of connectivity could result in misunderstandings across subdisciplines. Here, we unite ecological and evolutionary perspectives into a single unifying framework by advocating for connectivity to be conceptualized as a generational continuum. Within this framework, connectivity can be subdivided into three timescales: (1) within a generation (e.g., movement), (2) across one parent-offspring generation (e.g., dispersal), and (3) across two or more generations (e.g., gene flow), with each timescale determining the relevant context and dictating whether the connectivity has ecological or evolutionary consequences. Applying our framework to real-world connectivity questions can help to identify sampling limitations associated with a particular methodology, further develop research questions and hypotheses, and investigate eco-evolutionary feedback interactions that span the connectivity continuum. We hope this framework will serve as a foundation for conducting and communicating research across subdisciplines, resulting in a more holistic understanding of connectivity in natural systems. 

Marine area-based conservation measures including no-take zones (areas with no fishing allowed) are often designed through lengthy processes that aim to optimize for ecological and social objectives. Their (semi) permanence generates high stakes in what seems like a one-shot game. In this paper, we theoretically and empirically explore a model of short-term area-based conservation that prioritizes adaptive co-management: temporary areas closed to fishing, designed by the fishers they affect, approved by the government, and adapted every 5 years. In this model, no-take zones are adapted through learning and trust-building between fishers and government fisheries scientists. We use integrated social-ecological theory and a case study of a network of such fisheries closures (“fishing refugia”) in northwest Mexico to hypothesize a feedback loop between trust, design, and ecological outcomes. We argue that, with temporary and adaptive area-based management, social and ecological outcomes can be mutually reinforcing as long as initial designs are ecologically “good enough” and supported in the social-ecological context. This type of adaptive management also has the potential to adapt to climate change and other social-ecological changes. This feedback loop also predicts the dangerous possibility that low trust among stakeholders may lead to poor design, lack of ecological benefits, eroding confidence in the tool’s capacity, shrinking size, and even lower likelihood of social-ecological benefits. In our case, however, this did not occur, despite poor ecological design of some areas, likely due to buffering by social network effects and alternative benefits. We discuss both the potential and the danger of temporary area-based conservation measures as a learning tool for adaptive co-management and commoning. 

Given climatic uncertainty and human population growth, tracking the world's freshwater availability is essential. Fortunately, data necessary to identify surface water patterns are abundant. Wrangling these data into an analytically friendly format, however, can be difficult for researchers not experienced in data manipulation and high‐performance computing. To increase data accessibility, we developed the Global Lake area, Climate, and Population (GLCP) dataset. The GLCP offers annually aggregated surface area, temperature, precipitation, and human population estimates for over 1.42 million lakes globally between 1995 and 2015. Our dataset is peer‐reviewed and publicly available in a tabular format, enabling researchers with a range of skill levels to effectively work with the data. All aggregation procedures were performed within Google Earth Engine and R, empowering future users to replicate and modify scripts. Three case studies are presented to highlight concrete applications of the GLCP with emphasis on natural resource management at local, regional, and national scales.