An innovative statistical approach to constructing a readily comprehensible food web for a demersal fish community

French, Ben J.; Clarke, K.R.; Platell, M. E.; Potter, I. C.

doi:10.1016/j.ecss.2013.03.014

Cited by 13 publications

(23 citation statements)

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“…These trophic guilds consumed varying amounts of five principal prey groups (Mysida, Bivalvia, Polychaeta, Teleostei and other Crustacea), of which Mysida appeared particularly important. Trophic guild structure was arranged along two gradients of body size and vertical habitat that can act to minimize competition and niche overlap among predators, as observed in other estuarine and marine systems (Garrison & Link, ; Marancik & Hare, ; French et al , ). The present work demonstrates the basic trophic structuring of a large estuarine assemblage and the functional roles that different predators play within the ecosystem.…”

Section: Discussionmentioning

confidence: 99%

“…The relatively negligible dietary effect observed for sampling month was surprising, but is supported by Baird & Ulanowicz (1989) who documented a consistent seasonal topology of the Chesapeake Bay food web based on network analysis. Other studies have also documented a relatively small or non-significant seasonal effect on dietary structure of fish assemblages (Reum & Essington, 2008;Colloca et al, 2010;Bundy et al, 2011;French et al, 2013). Seasonal shifts in diets caused by changing prey availability and species migrations can indeed occur (Hajisamae & Ibrahim, 2008;Latour et al, 2008); however, the magnitude of these changes within the broader assemblage was relatively weak compared with dietary differences among species and size classes.…”

Section: I E Ta Ry Va R I a B I L I T Y A N D E X T R A P O L At I O Nmentioning

confidence: 99%

“…Bivalvia and Polychaeta are regularly identified as dominant prey groups for estuarine and marine fishes (Baldo & Drake, 2002;Reum & Essington, 2008;French et al, 2013). The relative specialization of some fishes on either of these two groups may be a common mechanism for partitioning macrobenthic resources.…”

Section: K E Y E S T Ua R I N E P R E Y G Ro U P Smentioning

confidence: 99%

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Diets and trophic‐guild structure of a diverse fish assemblage in Chesapeake Bay, U.S.A.

Buchheister

Latour

2015

Journal of Fish Biology

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Dietary habits and trophic-guild structure were examined in a fish assemblage (47 species) of the Chesapeake Bay estuary, U.S.A., using 10 years of data from >25 000 fish stomachs. The assemblage was comprised of 10 statistically significant trophic guilds that were principally differentiated by the relative amounts of Mysida, Bivalvia, Polychaeta, Teleostei and other Crustacea in the diets. These guilds were broadly aggregated into five trophic categories: piscivores, zooplanktivores, benthivores, crustacivores and miscellaneous consumers. Food web structure was largely dictated by gradients in habitat (benthic to pelagic) and prey size. Size classes within piscivorous species were more likely to be classified into different guilds, reflecting stronger dietary changes through ontogeny relative to benthivores and other guilds. Relative to predator species and predator size, the month of sampling had negligible effects on dietary differences within the assemblage. A majority of sampled fishes derived most of their nutrition from non-pelagic prey sources, suggesting a strong coupling of fish production to benthic and demersal food resources. Mysida (predominantly the opossum shrimp Neomysis americana) contributed substantially to the diets of over 25% of the sampled predator groups, indicating that this species is a critical, but underappreciated, node in the Chesapeake Bay food web.

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Section: Discussionmentioning

confidence: 99%

Section: I E Ta Ry Va R I a B I L I T Y A N D E X T R A P O L At I O Nmentioning

confidence: 99%

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Diets and trophic‐guild structure of a diverse fish assemblage in Chesapeake Bay, U.S.A.

Buchheister

Latour

2015

Journal of Fish Biology

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“…In the reviewed studies, prey items were analyzed either individually or were aggregated into groups according to taxonomic status, functional characteristics, body size (e.g., Oakley et al, 2014), life history stage (e.g., Whitehouse et al, 2017), or association with different habitat types and depth (e.g., Giraldo et al, 2017). Similarly, the biological traits of the sampled predators, such as body or gape size, and ontogenetic shifts in feeding (e.g., Abdurahiman et al, 2010;French et al, 2013;Dunic and Baum, 2017;Hanson, 2018) were taken into account in some studies, to allow a more detailed assessment of the feeding habits of a species. The consideration of such biotic traits were sometimes also used to aggregate species into trophic guilds (non-taxonomic groups of species which exploit the same resources; Abdurahiman et al, 2010;Whitehouse et al, 2017;Hanson, 2018), estimate trophic spectra (e.g., Torruco et al, 2007), and calculate indices which were used as basic inputs in respective food web models (e.g., the consumption per biomass ratio; Ullah et al, 2018).…”

Section: Methodological Aspectsmentioning

confidence: 99%

Global Systematic Review of Methodological Approaches to Analyze Coastal Shelf Food Webs

et al. 2020

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“…() paper had 279 citations. Publications utilizing SIMPROF tend to come from marine ecology, with studies focusing on beta‐diversity in reef corals (Huang et al., ), diatoms (Hernandez Almeida & Siqueiros Beltrones, ), fishes (Macedo‐Soares, Freire, & Muelbert, ; Selleslagh et al., ), fish gut contents (French, Clarke, Platell, & Potter, ), macrofauna (Rehm, Hooke, & Thatje, ), and sediment microbes (Gilbert et al., ). SIMPROF‐based studies have also been conducted on dinoflagellates and ciguatera poisoning (Parsons, Settlemier, & Ballauer, ), food webs (Kelly & Scheibling, ), habitat classifications (Gonzalez‐Mirelis & Buhl‐Mortensen, ; Valesini, Hourston, Wildsmith, Coen, & Potter, ), species/environment relationships (Travers, Potter, Clarke, & Newman, ), metagenomics (Khodakova, Smith, Burgoyne, Abarno, & Linacre, ), and otolith elemental microchemistry (Moore & Simpfendorfer, ).…”

Section: Introductionmentioning

confidence: 99%

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure

Kilborn

Jones

Peebles

et al. 2017

Ecology and Evolution

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Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing‐based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance‐based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

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An innovative statistical approach to constructing a readily comprehensible food web for a demersal fish community

Cited by 13 publications

References 50 publications

Diets and trophic‐guild structure of a diverse fish assemblage in Chesapeake Bay, U.S.A.

Diets and trophic‐guild structure of a diverse fish assemblage in Chesapeake Bay, U.S.A.

Global Systematic Review of Methodological Approaches to Analyze Coastal Shelf Food Webs

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure

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