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Top-predator recovery abates geomorphic decline of a coastal ecosystem

Nature volume 626, pages 111–118 (2024)Cite this article

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Abstract

The recovery of top predators is thought to have cascading effects on vegetated ecosystems and their geomorphology1,2, but the evidence for this remains correlational and intensely debated3,4. Here we combine observational and experimental data to reveal that recolonization of sea otters in a US estuary generates a trophic cascade that facilitates coastal wetland plant biomass and suppresses the erosion of marsh edges—a process that otherwise leads to the severe loss of habitats and ecosystem services5,6. Monitoring of the Elkhorn Slough estuary over several decades suggested top-down control in the system, because the erosion of salt marsh edges has generally slowed with increasing sea otter abundance, despite the consistently increasing physical stress in the system (that is, nutrient loading, sea-level rise and tidal scour7,8,9). Predator-exclusion experiments in five marsh creeks revealed that sea otters suppress the abundance of burrowing crabs, a top-down effect that cascades to both increase marsh edge strength and reduce marsh erosion. Multi-creek surveys comparing marsh creeks pre- and post-sea otter colonization confirmed the presence of an interaction between the keystone sea otter, burrowing crabs and marsh creeks, demonstrating the spatial generality of predator control of ecosystem edge processes: densities of burrowing crabs and edge erosion have declined markedly in creeks that have high levels of sea otter recolonization. These results show that trophic downgrading could be a strong but underappreciated contributor to the loss of coastal wetlands, and suggest that restoring top predators can help to re-establish geomorphic stability.

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Fig. 1: Study system and long-term trends in sea otter abundance and creekbank erosion.
Fig. 2: Locations of experimental and observational studies and results from a predator-exclusion experiment.
Fig. 3: Results from an analysis of tidal creeks comparing pre- and post-expansion of sea otters, examining relationships between sea otters, salt marsh biomass and creekbank retreat.
Fig. 4: Relationships between sea otter abundance, shore crab consumption and creekbank erosion in Elkhorn Slough from 2013 to 2015.

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Data availability

All raw data files and code are available at https://github.com/bbhughes/otters-erosion.

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Acknowledgements

B.B.H. was funded through the David H. Smith Research Conservation Fellowship and Cedar Tree Foundation and the Rebecca and Steve Sooy Fellowship in Marine Mammals; C.A. was supported by an NSF CAREER award (1652628); and B.R.S. was supported by the Stolarz Foundation, the Lenfest Ocean Program, Foundation of the Carolinas and an NSF CAREER award. We thank P. Daleo and M. Hensel for constructive comments on this manuscript. We also thank the staff and volunteers of the Monterey Bay Aquarium Sea Otter Program and Elkhorn Slough National Estuarine Research Reserve who contributed to sea otter abundance and foraging-data collection and experimental data collection. We dedicate this paper to J. Estes, whose pioneering career and mentorship were crucial to developing this research.

Author information

Authors and Affiliations

  1. Department of Biology, Sonoma State University, Rohnert Park, CA, USA

    Brent B. Hughes & Madeline Sanchez

  2. Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University, Beaufort, NC, USA

    Brent B. Hughes & Brian R. Silliman

  3. Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, USA

    Kathryn M. Beheshti, M. Tim Tinker & Sarah Espinosa

  4. Marine Science Institute, University of California Santa Barbara, Santa Barbara, CA, USA

    Kathryn M. Beheshti

  5. Nhydra Ecological Research, Head of St Margarets Bay, Nova Scotia, Canada

    M. Tim Tinker

  6. Department of Environmental Engineering Sciences, Engineering School for Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL, USA

    Christine Angelini

  7. Moss Landing Marine Laboratories, Geological Oceanography Lab, Moss Landing, CA, USA

    Charlie Endris

  8. Division of Regional Assistance, California Department of Water Resources, West Sacramento, CA, USA

    Lee Murai

  9. Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, British Columbia, Canada

    Sean C. Anderson

  10. Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada

    Sean C. Anderson

  11. Monterey Bay Aquarium, Monterey, CA, USA

    Michelle Staedler

  12. Western Ecological Research Center, U.S. Geological Survey, Santa Cruz, CA, USA

    Joseph A. Tomoleoni

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Contributions

B.B.H. conceived the idea, which was enhanced by discussions with B.R.S. B.B.H., B.R.S., K.M.B. and C.A. designed salt marsh and crab surveys and experiments. B.B.H. and K.M.B. collected salt marsh and crab data. C.E. and L.M. collected and analysed data for salt marsh and creek aerial data. M.T.T., M. Sanchez, M. Staedler, S.E. and J.A.T. designed and collected sea otter monitoring data. B.B.H., M.T.T. and S.C.A. analysed sea otter data. B.B.H. ran all other statistical analysis with guidance from B.R.S., S.C.A. and C.A. All authors contributed to editing and writing.

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Correspondence to Brent B. Hughes.

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Extended data figures and tables

Extended Data Fig. 1 Results from a three-year sea otter-exclusion experiment testing the effects of otters and shore crabs on salt marsh vegetation.

a–d, Response variables measured are crab burrow density (a), bulk density (ww = wet weight) (b), sediment accretion (dw = dry weight) (c), and crab burrows (d). Crab densities (Extended Data Figs. 1 and 2) were sampled over three years (measured in number of shore crab per 2 m2), the other response variables were sampled in year 2. The bars represent the mean, points are the data, and lines represent the differences in paired blocks (with the exception of d, which compares crab burrows in procedural controls and plots (n = 5) within the same creek, but are not paired). *P < 0.05 for cage treatments using linear mixed models (continuous response data) and generalized linear mixed models (count response data) with assigning paired experimental plots (block) and site (n = 5) as random factors. NS, not significant.

Extended Data Fig. 2 Changes in shore crab densities when compared to the first survey in May 2014.

a, Open circles represent counts for individual plots and lines connect the same plots through time. Solid circles and vertical line segments represent the estimated mean and 95% CI for each sampling event. There was a small but significantly greater change in density of shore crab in No Otter plots compared to Otter plots when accounting for time (P = 0.047). b,c, Smoothers for day since start (b) and month used in the GAMM (c). Note: Final shore crab densities were analysed in December 2016 (see Extended Data Fig. 1a).

Extended Data Fig. 3 Example of camera-trap data.

Here a sea otter is consuming a shore crab from our Procedural Control plots in one of the experimental creeks (top white box). The bottom image is a zoomed-in screenshot of the same foraging bout highlighted in the box with the sea otter floating over the Procedural Control. Shore crabs are the only known prey item for sea otters in pickleweed marshes. Photo credit: M. Sanchez.

Extended Data Fig. 4 Results of a shore crab feeding experiment on pickleweed aboveground and belowground biomass.

Each point represents a measurement of single-crab consumption (in fresh weight, fw) over a 72-h period. Controls had vegetation without crabs. Letters indicate significant differences (P < 0.05) using independent samples t-tests.

Extended Data Fig. 5 Results of a survey examining the relationship between shore crabs and sea otters in tidal creeks, and leverage analysis between sea otter crab consumption and erosion.

a, Results from a 2015 survey of crab densities on the marsh edge. Sea otter density was estimated using survey data from 2013−2015 in each replicate creek. Each point represents a mean along 100-m transects (n = 5 plots per creek) from 13 tidal creeks; grey areas represent ±95% CI, *P < 0.05. b, Leverage analysis comparing the influence of each creek on the slope of the relationship between sea otter crab consumption and erosion.

Extended Data Fig. 6 Modelling erosion rates.

Left, erosion rate modelled as a function of time with a GAM. Individual lines represent 200 samples from the posterior distribution. Right, each posterior sample is converted to a starting creek width value of 1. The vertical lines indicate the year the second-stage model started (1992).

Extended Data Fig. 7 Illustration of the modelled reduction in the base rate of creek erosion as the number of otters increases.

The shape follows a Gompertz curve defined as implied by (1 − exp(−bO)), in which O represents the number of sea otters and b represents an estimated parameter. The line and ribbon indicate the median and 95% credible interval.

Extended Data Fig. 8 Posterior parameter distributions from modelling relative creek width as a function of a base rate of widening and an adjustment given the abundance of sea otters.

This model is referred to as the ‘second-stage model’ in the Methods. In this figure, the model is fitted to mean values from the first-stage model (that is, not propagating uncertainty) as an illustration. The full model used for inference is fitted to 200 samples from the first-stage model. Histograms on the diagonal show the distribution for an individual parameter and the off-diagonals show the bivariate distribution for pairs of parameters. The parameter ‘r’ is the base rate of widening, ‘a’ is the relative channel width in the first year, ‘b’ is the maximum reduction in widening per otter and ‘sigma’ is the lognormal observation error standard deviation.

Extended Data Fig. 9 Histograms of b and r posterior distributions from the second-stage model (while propagating uncertainty from the first model).

The parameter b represents the maximum reduction in widening per otter and r represents the base rate of widening.

Extended Data Fig. 10 Model outputs describing creek changes in width relative to the starting year and erosion accounting for sea otters.

Left, values of \({\hat{W}}_{t}\) (relative creek width) from the first-stage model (blue dots = means; blue line segments = 95% credible intervals) and predicted values μt (black line = median; grey ribbon = 95% CI) from the second-stage model. Year increments start with year 1 as 1992. Right, the effective annual rate of erosion accounting for sea otters (reff) through time as determined by the estimates of r (base rate of widening), b (maximum reduction in widening per sea otter) and the number of sea otters. Line and ribbon indicate median and 95% credible interval.

Supplementary information

Reporting Summary

Supplementary Table 1

Camera trapping of sea otter behaviour. Camera trap data collected in 2020 from two experimental sites documenting sea otter behaviours and foraging around experiment and non-experimental locations.

Supplementary Table 2

Summary of all datasets used in the study. This includes information about time period, spatial scale, sample frequency, sample size, the method and purpose for data collection, the response reported, the corresponding figure, and the file name as it appears on the data repository: https://github.com/bbhughes/otters-erosion.

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Hughes, B.B., Beheshti, K.M., Tinker, M.T. et al. Top-predator recovery abates geomorphic decline of a coastal ecosystem. Nature 626, 111–118 (2024). https://doi.org/10.1038/s41586-023-06959-9

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