Limiting factors for queen conch (Lobatus gigas) reproduction: A simulation-based evaluation

Abstract

Queen conch are among the most economically, socially, and culturally important fishery resources in the Caribbean. Despite a multitude of fisheries management measures enacted across the region, populations are depleted and failing to recover. It is believed that queen conch are highly susceptible to depensatory processes, impacting reproductive success and contributing to the lack of recovery. We developed a model of reproductive dynamics to evaluate how variations in biological factors such as population density, movement speeds, rest periods between mating events, scent tracking, visual perception of conspecifics, sexual facilitation, and barriers to movement affect reproductive success and overall reproductive output. We compared simulation results to empirical observations of mating and spawning frequencies from conch populations in the central Bahamas and Florida Keys. Our results confirm that low probability of mate finding associated with decreased population density is the primary driver behind observed breeding behavior in the field, but is insufficient to explain observed trends. Specifically, sexual facilitation coupled with differences in movement speeds and ability to perceive conspecifics may explain the observed lack of mating at low densities and differences between mating frequencies in the central Bahamas and Florida Keys, respectively. Our simulations suggest that effective management strategies for queen conch should aim to protect high-density reproductive aggregations and critical breeding habitats.

Fig 1. Percent mating relative to model parameters.

Mean (solid lines) and 95% confidence band (shaded ribbons) for percent of simulated adult queen conch successfully mating relative to log₁₀ adult density (No./ha) relative to variation in A) movement speed (V), B) interbreeding rest period (RP), C) scent tracking (T), D) perception distance (PD), E) sexual facilitation (SF), and F) barriers to movement (B), with all other variables fixed at their lowest simulated values. Empirical observations by Stoner & Ray-Culp [32] and Stoner et al. [34] (black crosses) and Delgado & Glazer [38] (purple x’s) are overlaid for comparison.

Fig 2. Percent spawning relative to model parameters.

Mean (solid lines) and 95% confidence band (shaded ribbons) for percent of simulated adult queen conch spawning relative to log₁₀ adult density (No./ha) relative to variation in A) movement speed (V), B) interbreeding rest period (RP), C) scent tracking (T), D) perception distance (PD), E) sexual facilitation (SF), and F) barriers to movement (B), with all other variables fixed at their lowest simulated values. Empirical observations by Stoner & Ray-Culp [32] and Stoner et al. [34] (black crosses) and Delgado & Glazer [38] (purple x’s) are overlaid for comparison.

Fig 3. Simulation parameters resulting in superior model fits to empirical observations.

Boxplots showing distribution of mechanistic simulation parameters across all simulations compared to distribution of parameters for simulations resulting in superior fits to empirical observations by Stoner & Ray-Culp [32] and Delgado & Glazer [38] as compared to logistic regression dose-response functions fit to the same data.

Fig 4. Simulated reproductive events relative to empirical observations.

Simulation model outputs for percent of simulated adult queen conch successfully A) mating and B) spawning, relative to log₁₀ adult density (No./ha) for all simulations (gray) and simulations providing superior fits (when non-mating activity above 100 adults/ha were excluded) to empirical data than logistic dose-response functions [54] fit to empirical observations by Stoner & Ray-Culp [32] and Stoner et al. [34] (black line and black crosses) and Delgado & Glazer [38] (purple line and purple x’s).

Fig 5. Mating and spawning activity.

Relationship between mating and spawning activity across model simulations providing superior fits to empirical data (line and 95% confidence band in red) with logistic dose-response function fit (blue line) and 95% confidence band (blue), relative to empirical observations by Stoner & Ray-Culp [32] (black crosses) and Delgado & Glazer [38] (purple x’s).