Seabird diets as bioindicators of Atlantic herring recruitment and stock size: a new tool for ecosystem-based fisheries management

Abstract

Ecosystem-based fishery management requires understanding of relationships between exploited fish and their predators, such as seabirds. We used exploratory regression analyses to model relationships between Atlantic herring (Clupea harengus) in the diet of seabird chicks at nine nesting colonies in the Gulf of Maine and four types of fishery-and survey-derived herring data. We found several strong relationships, which suggests spatial structuring in herring stocks and likely patterns of herring movements before they recruit into the fishery. Some types of herring data seldom used in stock assessments -notably acoustic surveys, fixed-gear landings, and mass-at-age (i.e., weight-at-age) -correlated as strongly with seabird data as more commonly used series, such as mobile-gear landings and modeled spawning stock biomass. Seabird chick diets collected at specific locations thus offer a promising means to assess the size, distribution, and abundance of juvenile herring across a broad area prior to recruitment, which is a major source of uncertainty in fisheries. Common terns (Sterna hirundo) showed the most potential as a bioindicator, correlating well and showing consistent spatial patterns with 11 of 13 fishery data series.Résumé : La gestion écosystémique des pêches nécessite une compréhension des liens entre les poissons exploités et leurs prédateurs, tels que les oiseaux marins. Nous avons utilisé des analyses de régression exploratoires pour modéliser les relations entre le hareng de l'Atlantique (Clupea harengus) dans l'alimentation de jeunes oiseaux de mer dans neuf colonies de nidification dans le golfe du Maine, et quatre types de données sur les harengs issues de la pêche et de campagnes d'évaluation. Nous avons relevé plusieurs relations marquées qui indiqueraient la présence d'une structure spatiale dans les stocks de harengs et de probables motifs de déplacement des harengs avant leur recrutement dans la pêche. Certains types de données sur les harengs rarement utilisés dans les évaluations des stocks, dont des données de relevés acoustiques, de débarquements de navires à engins fixes et de poids selon l'âge, présentent une corrélation tout aussi forte avec les données sur les oiseaux de mer que des séries plus couramment utilisées, comme les débarquements de navires à engins mobiles et la biomasse modélisée du stock reproducteur. Les régimes alimentaires des jeunes oiseaux de mer prélevés en des sites précis constituent donc une approche prometteuse pour évaluer la taille, la répartition et l'abondance des harengs juvéniles avant leur recrutement à l'échelle d'une grande région, des facteurs qui constituent une importante source d'incertitude dans les pêches. Les sternes pierregarins (Sterna hirundo) montrent le meilleur potentiel comme bioindicateur, puisqu'elles présentent une bonne corrélation et des motifs spatiaux concordants avec 11 des 13 séries de données issues de la pêche. [Traduit par la Rédaction]

Marine top predators as climate and ecosystem sentinels

“…Species that either exhibit a clear mechanistic link to unobserved ecosystem processes (Scopel et al . ) or act as sentinels across multiple spatial or temporal scales (Boersma ; Moore ) should be the subject of long‐term monitoring programs, and the data derived from such programs then need to be translated into management action (Samhouri et al . ; Hays et al .…”

“…We have presented examples of marine predators that can act as sentinels of unobserved ecosystem change and have developed a framework for identifying useful ecosystem sentinels -one that could also be adapted to aquatic and terrestrial systems. Species that either exhibit a clear mechanistic link to unobserved ecosystem processes (Scopel et al 2017) or act as sentinels across multiple spatial or temporal scales (Boersma 2008;Moore 2008) should be the subject of long-term monitoring programs, and the data derived from such programs then need to be translated into management action (Samhouri et al 2017;Hays et al 2019). Where possible, future studies should focus on identifying the mechanistic links between sentinels and the broader ecosystem.…”

Atlantic puffin diet reflects haddock and redfish abundance in the Gulf of Maine

“…There is a growing body of research on the use of sea birds as indicators in fisheries management (Velarde et al 2015, Brisson-Curadeau et al 2017, Scopel et al 2018. Seabirds often feed on ecologically and commercially important forage fish.…”

“…In particular, a number of studies have proposed the use of puffins as indicators of forage fish availability (Hatch & Sanger 1992, Diamond & Devlin 2003, Cunningham et al 2018, including in regions where there are no independent estimates of stock size or parameters (Sydeman et al 2017, Piatt et al 2018. Several studies have shown relationships between seabird diet composition and independent measures of forage fish abundance (Barrett 2002, Deguchi et al 2004, Mills et al 2007, Velarde et al 2015, Scopel et al 2018). In the case of Atlantic puffins Fratercula arctica, the proportions of capelin Mallotus villosus, age-1 herring Clupea harengus, and age-0 cod Gadus morhua in puffin diet were proportional to their relative estimated biomasses in Norway (Barrett 2002), and the proportion of age-1 herring in puffin diet was proportional to age-1 re cruitment of herring in the Gulf of Maine (GOM) (Scopel et al 2018).…”

Wildlife usage indicates increased similarity between reclaimed upland habitat and mature boreal forest in the Athabasca Oil Sands Region of Alberta, Canada

“…Potential indicators may include habitat attributes, keystone species, species at risk, species associated with specific habitat requirements, or species that can be monitored easily [31, 33, 34]. Critically, their selection should also be appropriate to the spatial scale of the applied management activity [35, 36]. Selection of indicators must also take into consideration factors external to the monitoring program, such as inter and intra-specific competition, predation, disease, and seasonal variation in temperature and precipitation rates [31–34, 37].…”