Streaming Media
Presentation Abstract
Mapping bivalve biomass through the use of GIS can be a valuable tool for managers of commercially and recreationally-important clam species. The Swinomish Indian Tribal Community has been conducting intertidal clam surveys on reservation beaches since the early 2000s with the goal of managing their bivalve fisheries sustainably. These data, however, can also be used to investigate how environmental factors may influence spatial clustering of target species. We mapped biomass data of Leukoma staminea, Saxidomus gigantea, Clinocardium nuttallii, and Tresus sp. on a culturally-valuable beach located on the reservation. Statistically significant clusters of biomass polygons were identified using a Getis-Ord Gi* Hotspot Analysis. Results give a pronounced and distinct picture of distribution and clustering of specific clam species for a particular period of time. In an attempt to identify potential driving factors of spatial clusters, multiple covariate datasets were compared with the biomass hotspot layer of each clam species. The following covariates were evaluated in the model: elevation, distance from harvester access, slope, aspect, distance from drainage features, fetch, and substrate. In order to validate correlations, each resulting dataset comparing a clam species to a variable was run through a generalized additive model. Results show significant covariates and their impact vary depending on species, further stressing the importance of species-specific management. For example, we found that S. gigantea clusters were likely to be negatively influenced by shorter distances of fetch whereas C. nuttallii were influenced positively by close proximity to drainage features. This type of analysis can assist managers by identifying important local environmental factors related to the generation of clam hotspots.
Session Title
Shellfish Galore
Conference Track
SSE9: Nearshore
Conference Name
Salish Sea Ecosystem Conference (2022 : Online)
Document Type
Event
SSEC Identifier
SSE-traditionals-209
Start Date
26-4-2022 1:30 PM
End Date
26-4-2022 3:00 PM
Type of Presentation
Oral
Genre/Form
conference proceedings; presentations (communicative events)
Contributing Repository
Digital content made available by University Archives, Heritage Resources, Western Libraries, Western Washington University.
Subjects – Topical (LCSH)
Clams--Salish Sea (B.C. and Wash.); Spatial ecology--Salish Sea (B.C. and Wash.); Clam populations--Salish Sea (B.C. and Wash.)
Geographic Coverage
Salish Sea (B.C. and Wash.)
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author's written permission.
Type
Text
Language
English
Format
application/pdf
Included in
Fresh Water Studies Commons, Marine Biology Commons, Natural Resources and Conservation Commons, Terrestrial and Aquatic Ecology Commons
AN ANALYSIS OF ENVIRONMENTAL FACTORS INFLUENCING SPATIAL CLUSTERING OF SALISH SEA CLAM SPECIES
Mapping bivalve biomass through the use of GIS can be a valuable tool for managers of commercially and recreationally-important clam species. The Swinomish Indian Tribal Community has been conducting intertidal clam surveys on reservation beaches since the early 2000s with the goal of managing their bivalve fisheries sustainably. These data, however, can also be used to investigate how environmental factors may influence spatial clustering of target species. We mapped biomass data of Leukoma staminea, Saxidomus gigantea, Clinocardium nuttallii, and Tresus sp. on a culturally-valuable beach located on the reservation. Statistically significant clusters of biomass polygons were identified using a Getis-Ord Gi* Hotspot Analysis. Results give a pronounced and distinct picture of distribution and clustering of specific clam species for a particular period of time. In an attempt to identify potential driving factors of spatial clusters, multiple covariate datasets were compared with the biomass hotspot layer of each clam species. The following covariates were evaluated in the model: elevation, distance from harvester access, slope, aspect, distance from drainage features, fetch, and substrate. In order to validate correlations, each resulting dataset comparing a clam species to a variable was run through a generalized additive model. Results show significant covariates and their impact vary depending on species, further stressing the importance of species-specific management. For example, we found that S. gigantea clusters were likely to be negatively influenced by shorter distances of fetch whereas C. nuttallii were influenced positively by close proximity to drainage features. This type of analysis can assist managers by identifying important local environmental factors related to the generation of clam hotspots.
