Space‐time investigation of the effects of fishing on fish populations

Ono, Kotaro; Shelton, Andrew O.; Ward, Eric J.; Thorson, James T.; Feist, Blake E.; Hilborn, Ray

doi:10.1890/14-1874

Cited by 12 publications

(13 citation statements)

References 47 publications

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“…; Ono et al . ), and to alternative time series structures. Recent studies have developed various approaches to analysing the spatial distribution of seagrasses (Coles, McKenzie & De'ath ; Grech & Coles ; March et al .…”

Section: Discussionmentioning

confidence: 99%

Forty years of seagrass population stability and resilience in an urbanizing estuary

et al. 2016

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Summary Coasts and estuaries contain among the most productive and ecologically important habitats in the world and face intense pressure from current and projected human activities, including coastal development. Seagrasses are a key habitat feature in many estuaries perceived to be in widespread decline owing to human actions. We use spatio‐temporal models and a 41‐year time series from 100s of km of shoreline which includes over 160 000 observations from Puget Sound, Washington, USA, to examine multiscale trends and drivers of eelgrass (Zostera spp.) change in an urbanizing estuary. At whole estuary scale (100s of km), we find a stable and resilient eelgrass population despite a more than doubling of human population density and multiple major climactic stressors (e.g. ENSO events) over the period. However, the aggregate trend is not reflected at the site scale (10s of km), where some sites persistently increase while others decline. Site trends were spatially asynchronous; adjacent sites sometimes exhibited opposite trends over the same period. Substantial change in eelgrass occurred at the subsite (0·1 km) scale, including both complete local loss and dramatic increase of eelgrass. Metrics of local human development including shoreline armouring, upland development (imperviousness) and human density provide no explanatory power for eelgrass population change at any spatial scale. Our results suggest that the appropriate scale for understanding eelgrass change is smaller than typically assumed (approximately 1‐ to 3‐km scale) and contrasts strongly with previous work. Synthesis. Despite ongoing conservation concern over seagrasses world‐wide, eelgrass in Puget Sound has been highly resilient to both anthropogenic and environmental change over four decades. Our work provides general methods that can be applied to understand spatial and temporal scales of change and can be used to assess hypothesized drivers of change.

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Section: Discussionmentioning

confidence: 99%

Forty years of seagrass population stability and resilience in an urbanizing estuary

et al. 2016

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“…When a = 0, ξ i,t is the sole representation of the spatial field for year t (i.e. the ‘time‐indep’ case – see Ono et al () for a similar approach). If a ≠ 0, the spatiotemporal field in t depends on the intensity and pattern of the field in t – 1 (i.e.…”

Section: Methodsmentioning

confidence: 99%

Can collective memories shape fish distributions? A test, linking space‐time occurrence models and population demographics

et al. 2017

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Social learning can be fundamental to cohesive group living, and schooling fishes have proven ideal test subjects for recent work in this field. For many species, both demographic factors, and inter‐ (and intra‐) generational information exchange are considered vital ingredients in how movement decisions are reached. Yet key information is often missing on the spatial outcomes of such decisions, and questions concerning how migratory traditions are influenced by collective memory, density‐dependent and density‐independent processes remain open. To explore these issues, we focused on Atlantic herring Clupea harengus, a long‐lived, dense‐schooling species of high commercial importance, noted for its unpredictable shifts in winter distribution, and developed a series of Bayesian space‐time occurrence models to investigate wintering dynamics over 23 yr, using point‐referenced fishery and survey records from Icelandic waters. We included covariates reflecting local‐scale environmental factors, temporally‐lagged prey biomass and recent fishing activity, and through an index capturing distributional persistence over time, derived two proxies for spatial memory of past wintering sites. The previous winter's occurrence pattern was a strong predictor of the present pattern, its influence increasing with adult population size. Although the mechanistic underpinnings of this result remain uncertain, we suggest that a ‘wisdom of the crowd’ dynamic may be at play, by which navigational accuracy towards traditional wintering sites improves in larger and/or denser, better synchronized schools. Wintering herring also preferred warmer, fresher, moderately stratified waters of lower velocity, close to hotspots of summer zooplankton biomass, our results indicative of heightened environmental sensitivity in younger cohorts. Incorporating spatiotemporal correlation structure and time‐varying regression coefficients improved model performance, and validation tests on independent observations one‐year ahead illustrate the potential of uniting demographic information and non‐stationary models to quantify both the strength of collective memory in animal groups and its relevance for the spatial management of populations.

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“…Since Lindgren and colleagues proved that a continuously indexed Gaussian field described by a Matérn covariance function can be represented as a discretely indexed Gaussian Markov random field (Rue and Held 2005;, rapid development of the SPDE approach within R-INLA has facilitated fitting of an expanding suite of hierarchical spatial and spatiotemporal models to spatial point patterns . This approach has recently proven useful in analyses of georeferenced fisheries data sets, which are often data-rich and where inference at the scale of point locations, rather than grids, is required (e.g., Ono et al 2016;.…”

Section: Appendix a Notes On The Modelling Approachmentioning

confidence: 99%

Combining multiple data sets to unravel the spatiotemporal dynamics of a data-limited fish stock

Pinto

Travers-Trolet

Macdonald

et al. 2019

Can. J. Fish. Aquat. Sci.

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The biological status of many commercially exploited fishes remains unknown, mostly due to a lack of data necessary for their assessment. Investigating the spatiotemporal dynamics of such species can lead to new insights into population processes and foster a path towards improved spatial management decisions. Here, we focused on striped red mullet (Mullus surmuletus), a widespread yet data-limited species of high commercial importance. Aiming to quantify range dynamics in this data-poor scenario, we combined fishery-dependent and -independent data sets through a series of Bayesian mixed-effects models designed to capture monthly and seasonal occurrence patterns near the species’ northern range limit across 20 years. Combining multiple data sets allowed us to cover the entire distribution of the northern population of M. surmuletus, exploring dynamics at different spatiotemporal scales and identifying key environmental drivers (i.e., sea surface temperature, salinity) that shape occurrence patterns. Our results demonstrate that even when process and (or) observation uncertainty is high, or when data are sparse, if we combine multiple data sets within a hierarchical modelling framework, accurate and useful spatial predictions can still be made.

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Space‐time investigation of the effects of fishing on fish populations

Cited by 12 publications

References 47 publications

Forty years of seagrass population stability and resilience in an urbanizing estuary

Forty years of seagrass population stability and resilience in an urbanizing estuary

Can collective memories shape fish distributions? A test, linking space‐time occurrence models and population demographics

Combining multiple data sets to unravel the spatiotemporal dynamics of a data-limited fish stock

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