A unifying framework for understanding ecological and evolutionary population connectivity

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

The global lake area, climate, and population dataset

An increasing population in conjunction with a changing climate necessitates a detailed understanding of water abundance at multiple spatial and temporal scales. Remote sensing has provided massive data volumes to track fluctuations in water quantity, yet contextualizing water abundance with other local, regional, and global trends remains challenging by often requiring large computational resources to combine multiple data sources into analytically-friendly formats. To bridge this gap and facilitate future freshwater research opportunities, we harmonized existing global datasets to create the Global Lake area, Climate, and Population (GLCP) dataset. The GLCP is a compilation of lake surface area for 1.42 + million lakes and reservoirs of at least 10 ha in size from 1995 to 2015 with co-located basin-level temperature, precipitation, and population data. The GLCP was created with FAIR (findable, accessible, interoperable, reusable) data principles in mind and retains unique identifiers from parent datasets to expedite interoperability. The GLCP offers critical data for basic and applied investigations of lake surface area and water quantity at local, regional, and global scales

When are bacteria really gazelles? Comparing patchy ecologies with dimensionless numbers.

From micro to planetary scales, spatial heterogeneity-patchiness-is ubiquitous in ecosystems, defining the environments in which organisms move and interact. However, most large-scale models still use spatially averaged 'mean fields' to represent natural populations, while fine-scale spatially explicit models are mostly restricted to particular organisms or systems. In a conceptual paper, Grünbaum (2012, Interface Focus 2: 150-155) introduced a heuristic, based on three dimensionless ratios quantifying movement, reproduction and resource consumption, to characterise patchy ecological interactions and identify when mean-field assumptions are justifiable. We calculated these dimensionless numbers for 33 interactions between consumers and their resource patches in terrestrial, aquatic and aerial environments. Consumers ranged in size from bacteria to whales, and patches lasted from minutes to millennia, with separation scales from mm to hundreds of km. No interactions could be accurately represented by naive mean-field models, though 19 (58%) could be partially simplified by averaging out movement, reproductive or consumption dynamics. Clustering interactions by their non-dimensional ratios revealed several unexpected dynamic similarities. For example, bacterial Pseudoalteromonas exploit nutrient plumes similarly to Mongolian gazelles grazing on ephemeral steppe vegetation. We argue that dimensional analysis is valuable for characterising ecological patchiness and can link widely different systems into a single quantitative framework

AEMON-J/DSOS Archive: "Hacking Limnology" Workshop + Virtual Summit in Data Science & Open Science in Aquatic Research

This OSF project is meant to serve as a long-term storage repository for presentations and workshop materials for the Aquatic Ecosystem Modeling-Junior (AEMON-J) and Virtual Summit: Incorporating Data Science and Open Science (DSOS) communities. Contributors in this repository include past presenters and workshop organizers. Contributors are only responsible for those individual presentations that are labeled with their surname