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Optimum lionfish yield: a non-traditional management concept for invasive lionfish (Pterois spp.) fisheries

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Abstract

This paper describes a non-traditional fisheries management concept and an indicator-based framework to encourage and guide management of invasive lionfish (Pterois spp.) fisheries in the temperate and tropical western Atlantic. We introduce the concept of optimum lionfish yield (OLY)—an extension of the concept of ecologically sustainable yield—which considers local ecological health in the establishment of fishery management targets. In contrast to traditional fishery targets, OLY is a target exceeding maximum sustainable yield (MSY) that still provides relatively high sustainable yield, but further contributes to population suppression beyond what is achievable through targets at or below MSY. Thus, OLY seeks to balance management trade-offs from both natural resource and invasive species management perspectives. In this study, we developed an age-structured population model and applied the concept of OLY to quantify targets to initiate management of a nationally-managed lionfish fishery in Belize. Socioeconomic and ecological data were used as indicators to formulate OLY values. The model indicates that lionfish in Belize are biologically robust to fishing pressure, which corroborates previous findings. Fishing lionfish at rates above MSY levels is expected to substantially reduce population abundance, much more so than fishing at rates below MSY levels, while having relatively minimal impacts on yield. Population suppression can be further enhanced by reducing size at selection, but this is expected to be done at a significant cost to landings. Together, these data support continued establishment of (managed) commercial lionfish fisheries throughout the invaded range to provide an alternative sustainable fishery resource and serve as a means of national- and international-level control. While the concept and framework described here is introduced for management of invasive lionfish, it could be applied to management of other invasive species, both aquatic and terrestrial.

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Acknowledgments

We thank T. Kwak, P. Tester, D. Nowacek, C. Harms, L. Lee, S. Binion-Rock, S. Gittings, M. Balling, A. Chester, G. Piniak, E. García-Berthou, M. Allen, and one anonymous reviewer for their insightful comments that improved the manuscript. We thank the Belize Coastal Zone Management Authority and Institute for the geospatial data used to estimate total initial abundance, and we thank Blue Ventures Expeditions staff and volunteers that collected lionfish data in Belize. Financial support was provided by The Joseph E. and Robin C. Hightower Endowment, The Mesoamerican Reef Fund, The Summit Foundation, NOAA Fisheries International Affair’s Program, and the U.S. Fish and Wildlife Service Aquatic Nuisance Species Grant program. The scientific results and conclusions, as well as any views and opinions expressed herein, are those of the authors and do not necessarily reflect those of any government agency.

Funding

Financial support was provided by The Joseph E. and Robin C. Hightower Endowment, The Mesoamerican Reef Fund, The Summit Foundation, NOAA Fisheries International Affair’s Program, and the U.S. Fish and Wildlife Service Aquatic Nuisance Species Grant program.

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Authors and Affiliations

  1. Department of Applied Ecology, North Carolina State University, Raleigh, NC, 27695, USA

    Alex K. Bogdanoff & Craig A. Layman

  2. Southeast Fisheries Science Center, NOAA, National Marine Fisheries Service, 101 Pivers Island Road, Beaufort, NC, 28516, USA

    Kyle W. Shertzer

  3. Blue Ventures Conservation, 39-41 North Road, London, N7 9DP, UK

    Jennifer K. Chapman & Marc L. Fruitema

  4. Department of Human Dimensions of Natural Resources, Colorado State University, Fort Collins, CO, 80523, USA

    Jennifer Solomon & Julie Sabattis

  5. Department of Biological Sciences, CW 405 Biological Sciences Building, University of Alberta, Edmonton, AB, T6G2E9, Canada

    Stephanie Green

  6. National Ocean Service, NOAA, National Centers for Coastal Ocean Science, 101 Pivers Island Road, Beaufort, NC, 28516, USA

    James A. Morris Jr

  7. Department of General Education, James Sprunt Community College, Kenansville, NC, 28349, USA

    Alex K. Bogdanoff

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  1. Alex K. Bogdanoff
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Correspondence to Alex K. Bogdanoff.

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Appendix: Additional analyses

Appendix: Additional analyses

Part 1: Exploring uncertainty in landings and abundance

As described in the main text, the 2015 field estimates of abundance and landings were 733,257 lionfish and 89,902 lionfish. Abundance and landings directly affect estimates of the current fishing mortality rate (current F) and the asymptotic recruitment of age-1 fish (R0) and, therefore, model output (see “Model Description” in the main text). Here, we explore uncertainty in our point estimates of abundance and landings on current F, R0, and model output.

To do this, we conducted Monte Carlo simulations (N = 2000 iterations in each analysis) in which each iteration repeated our analysis but with different values of (1) abundance and landings and then (2) current F and R0. First, we drew a new value of abundance and a new value of landings each from a normal distribution with mean equal to the 2015 field estimates and an assumed coefficient of variation (CV) of CV = 0.1 (Appendix Fig. 5a, b). Using these values, we computed distributions of current F (Appendix Fig. 5c) and of R0 (Appendix Fig. 5d). We then propagated uncertainty in current F and R0 into the estimated management quantities. Similar to above, we conducted Monte Carlo simulations (N = 2000 iterations) in which each iteration repeated our analysis but with different values of current F and R0 drawn from their distributions produced above (i.e., Appendix Fig. 5c, d). This produced distributions of current landings (Lcurrent), MSY, and OLY (Appendix Fig. 6a), as well as in the levels of abundance associated with those values (Appendix Fig. 6b). The general conclusion presented in the main text remains the same as that inferred from the corresponding point estimates—fishing at OLY provides current levels of landings (by design) while suppressing the abundance to substantially lower levels than current.

Fig. 5
figure 5

Assumed distributions of initial lionfish abundance (a) and landings (b) used to compute the current fishing rate (current F) (c) and the asymptotic recruitment of age-1 fish (R0) (d). Vertical lines indicate the 2015 field estimates of abundance and landings (a, b) and the point estimates of current F and R0 derived from those field estimates (c, d)

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Fig. 6
figure 6

Distributions of results derived from assumed distributions of field estimates of abundance and landings. Panel A shows distributions of landings corresponding to Lcurrent (blue), OLY (purple), and MSY (green). Note that Lcurrent is not apparent because it overlaps entirely with OLY (by design). Panel B shows the levels of abundance that correspond to the landings in Panel A

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We additionally propagated uncertainty in current F and R0 into estimates of equilibrium landings and abundance as a function of fishing rate (Appendix Fig. 7). We caution that this analysis does not produce true confidence bands as it is predicated on our assumed value of CV = 0.1. It does, however, indicate the conditional degree of uncertainty in results stemming from the field estimates of abundance and landings.

Fig. 7
figure 7

Equilibrium landings (a) and abundance (b) of lionfish in Belize across a range of fishing mortality rates: FMIN = 0 (filled square), FCURRENT = 0.32 (filled circle), FMSY = 0.67 (filled triangle), FOLY = 1.51 (filled diamond), and FHIGH = 5.0. Intervals shown represent the 2.5th and 97.5th percentiles from N = 2000 Monte Carlo simulations with variability in the 2015 field estimates of lionfish abundance and landings

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Part 2: Exploring Uncertainty in the standard deviation of size-at-age

As part of our analyses in the main text, we examined the effects of size-at-selection on equilibrium abundance and landings (see Fig. 4 in main text). The size-at-selection, along with growth characteristics including the standard deviation of size-at-age, determined the pattern of selectivity as the proportion of fish-at-age that were vulnerable to fishing. In Appendix Fig. 8, we show how size-at-selection and standard deviation of size-at-age affect the resulting selectivity curves. In general, the curves are far more sensitive to the size-at-selection (our pivot) than to the standard deviation.

Fig. 8
figure 8

Selectivity as a function of the size-at-selection (Lvuln) and the standard deviation (SD) of size-at-age. Our base values were Lvuln = 250 mm and SD = 28.2, and values used to create Fig. 4 (main text) varied Lvuln over the range 200 mm to 300 mm, with SD = 28.2 in all cases. For this figure, we additionally varied SD ± 25% of the base value

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Bogdanoff, A.K., Shertzer, K.W., Layman, C.A. et al. Optimum lionfish yield: a non-traditional management concept for invasive lionfish (Pterois spp.) fisheries. Biol Invasions 23, 795–810 (2021). https://doi.org/10.1007/s10530-020-02398-z

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