Crayfish Movement Project

Principle Investigator: Jacob Westhoff

Students and Staff: Augusto Huber

Start date: Fall 2022


1. Determine movement and dispersal capacity for one or two species of invasive crayfish in Missouri Ozark streams using passive integrated transponder tags and radio telemetry

2. Predict the spread of one or two crayfish invasions using individual-based forecast models

Project synopsis:

Invasive aquatic organisms are affecting natural communities across the globe and freshwater crayfishes are among some of the most notorious invaders. In Missouri alone, there are at least 32 documented crayfish invasions. Several of these invasions threaten rare native crayfishes such as the Big Creek Crayfish (Faxonius peruncus), the St. Francis River Crayfish (Faxonius quadruncus), and the Coldwater Crayfish (Faxonius eupunctus). There is a noticeable lack of published information regarding movement and dispersal capacity of freshwater crayfishes in North American lotic environments, thus complicating efforts to predict their spread and impact.

We will select sample sites to study crayfish movement based on presence and density of the invasive crayfish, landowner access, and desirable habitat conditions. We may select multiple sample sites (n <6) within a single drainage (e.g., upper St. Francis River drainage) or from multiple drainages (e.g., add the Eleven Point River drainage). At a given site we will tag adult crayfish using passive integrated transponder (PIT) tags and/or radio tags to examine movement capacity at both the individual and population levels. Ideally, tagged crayfish will be tracked during multiple seasons to document temporal patterns in movement and dispersal. Tracking will use a combination of stationary PIT antenna arrays and manual tracking via PIT transceivers and radio receivers.

We anticipate presenting summary statistics for average daily movement rates, movement direction, magnitude, and frequency. We may further explore movement relationships among populations, species, sizes, and sexes. Ultimately, we will create dispersal kernels (probability of individual moving a given distance from starting point) for different seasons and populations. Those dispersal kernels supply movement data to spatial explicit individual-based models capable of using life history data with spatially and temporally varying environmental data to mechanistically forecast invasion patterns. This modeling approach also allows for simulated investigation of density reduction methods as a management alternative.