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Louis Gross

Publications and source records attributed to Louis Gross.

2 recordsLinked to original sources

Maintaining momentum for collaborative working groups in a post-pandemic world

Scientific progress depends in part on our ability to synthesize heterogeneous data and ideas into new models and paradigms. In environmental sciences, such synthesis has been particularly effective when conducted by collaborative working groups: diverse groups of researchers and practitioners brought together for a concentrated period of collaboration on key questions. Such work is often done at synthesis centres: organizations that promote, fund, organize and host working groups and other collaborative research and training activities 1 . However, because of the COVID-19 pandemic, synthesis centres have had to rapidly adapt to supporting fully virtual working groups; the eight centres we direct supported 68 virtual working groups in the past year. Based on this experience, we conclude — contrary to a recent editorial on conferences published in this journal 2 — that virtual gatherings, while providing a bridge during the pandemic, cannot replace immersive, in-person collaborations. In-person working group meetings involve productive and varied interactions for many hours over consecutive days. We have found that virtual sessions lose effectiveness after a few hours, as participants become fatigued from staring at a screen or juggling local demands. While virtual meetings can work for short, well-delineated tasks, they are less suited for unstructured and free-flowing discussions — and thus struggle to create the social cohesion and trust known to fuel creative breakthroughs during week-long in-person meetings 3 .

Nature Ecology and Evolution

Evaluation of and insights from ALFISH: a spatially explicit landscape-level simulation of fish populations in the Everglades

We present an evaluation of a spatially explicit, age-structured model created to assess fish density dynamics in the Florida Everglades area. This model, ALFISH, has been used to compare alternative management scenarios for the Florida Everglades region. This area is characterized by periodic dry downs and refloodings. ALFISH uses spatially explicit water depth data to predict patterns of fish density. Here we present a method for calibration of ALFISH, based on information concerning fish movement, pond locations and other field data. With the current information, the greatest coefficient of determination achieved from regressions of ALFISH output to field data is 0.35 for fish density and 0.88 for water depth. The poor predictability of fish density mirrors the empirical findings that hydrology, which is the main driver of the model, only accounts for 20–40% of the variance of fish densities across the Everglades landscape. Sensitivity analyses indicate that fish in this system are very sensitive to frequency, size and location of permanent ponds as well as availability of prey.

Florida